Overview

Dataset statistics

Number of variables80
Number of observations1459
Missing cells7878
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory912.0 KiB
Average record size in memory640.1 B

Variable types

Numeric30
Categorical48
Boolean1
Text1

Dataset

DescriptionAutomated EDA report generated by Team A pipeline
URL

Alerts

Utilities has constant value "AllPub"Constant
Id is highly overall correlated with Alley and 3 other fieldsHigh correlation
MSSubClass is highly overall correlated with LotFrontage and 12 other fieldsHigh correlation
MSZoning is highly overall correlated with Alley and 1 other fieldsHigh correlation
LotFrontage is highly overall correlated with MSSubClass and 6 other fieldsHigh correlation
LotArea is highly overall correlated with MSZoning and 12 other fieldsHigh correlation
Alley is highly overall correlated with BldgType and 8 other fieldsHigh correlation
LotShape is highly overall correlated with MiscFeatureHigh correlation
LandContour is highly overall correlated with NeighborhoodHigh correlation
LotConfig is highly overall correlated with PoolQCHigh correlation
LandSlope is highly overall correlated with MiscFeatureHigh correlation
Neighborhood is highly overall correlated with Alley and 2 other fieldsHigh correlation
Condition1 is highly overall correlated with Condition2High correlation
Condition2 is highly overall correlated with AlleyHigh correlation
BldgType is highly overall correlated with Alley and 1 other fieldsHigh correlation
HouseStyle is highly overall correlated with MSSubClass and 5 other fieldsHigh correlation
OverallQual is highly overall correlated with Alley and 22 other fieldsHigh correlation
OverallCond is highly overall correlated with Alley and 5 other fieldsHigh correlation
YearBuilt is highly overall correlated with MSSubClass and 26 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with Alley and 15 other fieldsHigh correlation
RoofStyle is highly overall correlated with RoofMatlHigh correlation
RoofMatl is highly overall correlated with RoofStyleHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
MasVnrType is highly overall correlated with Heating and 1 other fieldsHigh correlation
MasVnrArea is highly overall correlated with LotArea and 10 other fieldsHigh correlation
ExterQual is highly overall correlated with Alley and 1 other fieldsHigh correlation
ExterCond is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
Foundation is highly overall correlated with AlleyHigh correlation
BsmtQual is highly overall correlated with Alley and 1 other fieldsHigh correlation
BsmtCond is highly overall correlated with 3SsnPorchHigh correlation
BsmtExposure is highly overall correlated with ExterQual and 1 other fieldsHigh correlation
BsmtFinType1 is highly overall correlated with AlleyHigh correlation
BsmtFinSF1 is highly overall correlated with LotArea and 9 other fieldsHigh correlation
BsmtFinType2 is highly overall correlated with BsmtFinType1 and 1 other fieldsHigh correlation
BsmtFinSF2 is highly overall correlated with BsmtFinType2 and 1 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with TotalBsmtSFHigh correlation
TotalBsmtSF is highly overall correlated with LotArea and 11 other fieldsHigh correlation
Heating is highly overall correlated with MasVnrTypeHigh correlation
HeatingQC is highly overall correlated with Neighborhood and 6 other fieldsHigh correlation
CentralAir is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
Electrical is highly overall correlated with CentralAirHigh correlation
1stFlrSF is highly overall correlated with LotArea and 8 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with MSSubClass and 9 other fieldsHigh correlation
LowQualFinSF is highly overall correlated with EnclosedPorchHigh correlation
GrLivArea is highly overall correlated with MSSubClass and 17 other fieldsHigh correlation
BsmtFullBath is highly overall correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
BsmtHalfBath is highly overall correlated with KitchenAbvGr and 2 other fieldsHigh correlation
FullBath is highly overall correlated with Neighborhood and 9 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClass and 7 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with MSSubClass and 6 other fieldsHigh correlation
KitchenAbvGr is highly overall correlated with BldgTypeHigh correlation
KitchenQual is highly overall correlated with ExterQualHigh correlation
TotRmsAbvGrd is highly overall correlated with LotArea and 13 other fieldsHigh correlation
Fireplaces is highly overall correlated with Neighborhood and 3 other fieldsHigh correlation
FireplaceQu is highly overall correlated with Neighborhood and 2 other fieldsHigh correlation
GarageType is highly overall correlated with MSSubClass and 5 other fieldsHigh correlation
GarageYrBlt is highly overall correlated with MSZoning and 13 other fieldsHigh correlation
GarageFinish is highly overall correlated with MSSubClass and 11 other fieldsHigh correlation
GarageCars is highly overall correlated with Neighborhood and 12 other fieldsHigh correlation
GarageArea is highly overall correlated with Neighborhood and 10 other fieldsHigh correlation
GarageQual is highly overall correlated with CentralAirHigh correlation
PavedDrive is highly overall correlated with Neighborhood and 7 other fieldsHigh correlation
WoodDeckSF is highly overall correlated with BsmtFinSF2High correlation
OpenPorchSF is highly overall correlated with LotArea and 8 other fieldsHigh correlation
EnclosedPorch is highly overall correlated with LowQualFinSF and 1 other fieldsHigh correlation
3SsnPorch is highly overall correlated with BsmtCondHigh correlation
ScreenPorch is highly overall correlated with MiscFeatureHigh correlation
PoolArea is highly overall correlated with LotArea and 4 other fieldsHigh correlation
PoolQC is highly overall correlated with Id and 17 other fieldsHigh correlation
MiscFeature is highly overall correlated with LandSlope and 2 other fieldsHigh correlation
MiscVal is highly overall correlated with LotArea and 7 other fieldsHigh correlation
YrSold is highly overall correlated with Id and 1 other fieldsHigh correlation
SaleType is highly overall correlated with SaleConditionHigh correlation
SaleCondition is highly overall correlated with SaleTypeHigh correlation
Street is highly overall correlated with Alley and 1 other fieldsHigh correlation
MSZoning is highly imbalanced (54.3%)Imbalance
Street is highly imbalanced (96.1%)Imbalance
LandContour is highly imbalanced (68.9%)Imbalance
LandSlope is highly imbalanced (83.1%)Imbalance
Condition1 is highly imbalanced (70.4%)Imbalance
Condition2 is highly imbalanced (95.6%)Imbalance
BldgType is highly imbalanced (57.4%)Imbalance
RoofStyle is highly imbalanced (67.7%)Imbalance
RoofMatl is highly imbalanced (94.8%)Imbalance
ExterCond is highly imbalanced (68.8%)Imbalance
BsmtCond is highly imbalanced (74.4%)Imbalance
BsmtFinType2 is highly imbalanced (67.6%)Imbalance
Heating is highly imbalanced (95.8%)Imbalance
CentralAir is highly imbalanced (63.7%)Imbalance
Electrical is highly imbalanced (75.4%)Imbalance
BsmtHalfBath is highly imbalanced (77.8%)Imbalance
KitchenAbvGr is highly imbalanced (82.7%)Imbalance
Functional is highly imbalanced (82.3%)Imbalance
GarageQual is highly imbalanced (80.8%)Imbalance
GarageCond is highly imbalanced (87.9%)Imbalance
PavedDrive is highly imbalanced (63.8%)Imbalance
MiscFeature is highly imbalanced (64.8%)Imbalance
SaleType is highly imbalanced (73.9%)Imbalance
SaleCondition is highly imbalanced (62.4%)Imbalance
LotFrontage has 227 (15.6%) missing valuesMissing
Alley has 1352 (92.7%) missing valuesMissing
MasVnrType has 894 (61.3%) missing valuesMissing
MasVnrArea has 15 (1.0%) missing valuesMissing
BsmtQual has 44 (3.0%) missing valuesMissing
BsmtCond has 45 (3.1%) missing valuesMissing
BsmtExposure has 44 (3.0%) missing valuesMissing
BsmtFinType1 has 42 (2.9%) missing valuesMissing
BsmtFinType2 has 42 (2.9%) missing valuesMissing
FireplaceQu has 730 (50.0%) missing valuesMissing
GarageType has 76 (5.2%) missing valuesMissing
GarageYrBlt has 78 (5.3%) missing valuesMissing
GarageFinish has 78 (5.3%) missing valuesMissing
GarageQual has 78 (5.3%) missing valuesMissing
GarageCond has 78 (5.3%) missing valuesMissing
PoolQC has 1456 (99.8%) missing valuesMissing
Fence has 1169 (80.1%) missing valuesMissing
MiscFeature has 1408 (96.5%) missing valuesMissing
PoolArea is highly skewed (γ1 = 20.19688759)Skewed
MiscVal is highly skewed (γ1 = 20.07518835)Skewed
Id is uniformly distributedUniform
Id has unique valuesUnique
MasVnrArea has 877 (60.1%) zerosZeros
BsmtFinSF1 has 462 (31.7%) zerosZeros
BsmtFinSF2 has 1278 (87.6%) zerosZeros
BsmtUnfSF has 123 (8.4%) zerosZeros
TotalBsmtSF has 41 (2.8%) zerosZeros
2ndFlrSF has 839 (57.5%) zerosZeros
LowQualFinSF has 1445 (99.0%) zerosZeros
GarageCars has 76 (5.2%) zerosZeros
GarageArea has 76 (5.2%) zerosZeros
WoodDeckSF has 762 (52.2%) zerosZeros
OpenPorchSF has 642 (44.0%) zerosZeros
EnclosedPorch has 1208 (82.8%) zerosZeros
3SsnPorch has 1446 (99.1%) zerosZeros
ScreenPorch has 1319 (90.4%) zerosZeros
PoolArea has 1453 (99.6%) zerosZeros
MiscVal has 1408 (96.5%) zerosZeros

Reproduction

Analysis started2025-06-03 11:16:44.286448
Analysis finished2025-06-03 11:16:50.023795
Duration5.74 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2190
Minimum1461
Maximum2919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:50.060263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1461
5-th percentile1533.9
Q11825.5
median2190
Q32554.5
95-th percentile2846.1
Maximum2919
Range1458
Interquartile range (IQR)729

Descriptive statistics

Standard deviation421.32133
Coefficient of variation (CV)0.19238417
Kurtosis-1.2
Mean2190
Median Absolute Deviation (MAD)365
Skewness0
Sum3195210
Variance177511.67
MonotonicityStrictly increasing
2025-06-03T13:16:50.121798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1461 1
 
0.1%
2441 1
 
0.1%
2439 1
 
0.1%
2438 1
 
0.1%
2437 1
 
0.1%
2436 1
 
0.1%
2435 1
 
0.1%
2434 1
 
0.1%
2433 1
 
0.1%
2432 1
 
0.1%
Other values (1449) 1449
99.3%
ValueCountFrequency (%)
1461 1
0.1%
1462 1
0.1%
1463 1
0.1%
1464 1
0.1%
1465 1
0.1%
1466 1
0.1%
1467 1
0.1%
1468 1
0.1%
1469 1
0.1%
1470 1
0.1%
ValueCountFrequency (%)
2919 1
0.1%
2918 1
0.1%
2917 1
0.1%
2916 1
0.1%
2915 1
0.1%
2914 1
0.1%
2913 1
0.1%
2912 1
0.1%
2911 1
0.1%
2910 1
0.1%

MSSubClass
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.378341
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:50.172411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.74688
Coefficient of variation (CV)0.74500027
Kurtosis1.3489675
Mean57.378341
Median Absolute Deviation (MAD)30
Skewness1.3466896
Sum83715
Variance1827.2957
MonotonicityNot monotonic
2025-06-03T13:16:50.218049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20 543
37.2%
60 276
18.9%
50 143
 
9.8%
120 95
 
6.5%
30 70
 
4.8%
70 68
 
4.7%
160 65
 
4.5%
80 60
 
4.1%
90 57
 
3.9%
190 31
 
2.1%
Other values (6) 51
 
3.5%
ValueCountFrequency (%)
20 543
37.2%
30 70
 
4.8%
40 2
 
0.1%
45 6
 
0.4%
50 143
 
9.8%
60 276
18.9%
70 68
 
4.7%
75 7
 
0.5%
80 60
 
4.1%
85 28
 
1.9%
ValueCountFrequency (%)
190 31
 
2.1%
180 7
 
0.5%
160 65
4.5%
150 1
 
0.1%
120 95
6.5%
90 57
3.9%
85 28
 
1.9%
80 60
4.1%
75 7
 
0.5%
70 68
4.7%

MSZoning
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing4
Missing (%)0.3%
Memory size11.5 KiB
RL
1114 
RM
242 
FV
 
74
C (all)
 
15
RH
 
10

Length

Max length7
Median length2
Mean length2.0515464
Min length2

Characters and Unicode

Total characters2985
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRH
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1114
76.4%
RM 242
 
16.6%
FV 74
 
5.1%
C (all) 15
 
1.0%
RH 10
 
0.7%
(Missing) 4
 
0.3%

Length

2025-06-03T13:16:50.370997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:50.422977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rl 1114
75.8%
rm 242
 
16.5%
fv 74
 
5.0%
c 15
 
1.0%
all 15
 
1.0%
rh 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1366
45.8%
L 1114
37.3%
M 242
 
8.1%
F 74
 
2.5%
V 74
 
2.5%
l 30
 
1.0%
C 15
 
0.5%
15
 
0.5%
( 15
 
0.5%
a 15
 
0.5%
Other values (2) 25
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1366
45.8%
L 1114
37.3%
M 242
 
8.1%
F 74
 
2.5%
V 74
 
2.5%
l 30
 
1.0%
C 15
 
0.5%
15
 
0.5%
( 15
 
0.5%
a 15
 
0.5%
Other values (2) 25
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1366
45.8%
L 1114
37.3%
M 242
 
8.1%
F 74
 
2.5%
V 74
 
2.5%
l 30
 
1.0%
C 15
 
0.5%
15
 
0.5%
( 15
 
0.5%
a 15
 
0.5%
Other values (2) 25
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1366
45.8%
L 1114
37.3%
M 242
 
8.1%
F 74
 
2.5%
V 74
 
2.5%
l 30
 
1.0%
C 15
 
0.5%
15
 
0.5%
( 15
 
0.5%
a 15
 
0.5%
Other values (2) 25
 
0.8%

LotFrontage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)9.3%
Missing227
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean68.580357
Minimum21
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:50.477526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile27.1
Q158
median67
Q380
95-th percentile107.45
Maximum200
Range179
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.376841
Coefficient of variation (CV)0.32628645
Kurtosis2.5872162
Mean68.580357
Median Absolute Deviation (MAD)12.5
Skewness0.66192107
Sum84491
Variance500.72303
MonotonicityNot monotonic
2025-06-03T13:16:50.537963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 133
 
9.1%
80 68
 
4.7%
70 63
 
4.3%
50 60
 
4.1%
75 52
 
3.6%
65 49
 
3.4%
85 36
 
2.5%
63 30
 
2.1%
24 30
 
2.1%
21 27
 
1.9%
Other values (105) 684
46.9%
(Missing) 227
 
15.6%
ValueCountFrequency (%)
21 27
1.9%
22 1
 
0.1%
24 30
2.1%
25 1
 
0.1%
26 3
 
0.2%
28 1
 
0.1%
30 5
 
0.3%
31 1
 
0.1%
32 3
 
0.2%
33 2
 
0.1%
ValueCountFrequency (%)
200 1
0.1%
195 1
0.1%
160 2
0.1%
155 1
0.1%
150 1
0.1%
149 1
0.1%
140 1
0.1%
136 1
0.1%
135 1
0.1%
134 1
0.1%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1106
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9819.1611
Minimum1470
Maximum56600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:50.594972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1470
5-th percentile3085.5
Q17391
median9399
Q311517.5
95-th percentile16873
Maximum56600
Range55130
Interquartile range (IQR)4126.5

Descriptive statistics

Standard deviation4955.5173
Coefficient of variation (CV)0.50467828
Kurtosis20.746549
Mean9819.1611
Median Absolute Deviation (MAD)2076
Skewness3.1152166
Sum14326156
Variance24557152
MonotonicityNot monotonic
2025-06-03T13:16:50.649914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 20
 
1.4%
7200 18
 
1.2%
6000 17
 
1.2%
9000 15
 
1.0%
7500 12
 
0.8%
10800 11
 
0.8%
6240 10
 
0.7%
7000 9
 
0.6%
6120 9
 
0.6%
1680 8
 
0.5%
Other values (1096) 1330
91.2%
ValueCountFrequency (%)
1470 1
0.1%
1476 1
0.1%
1477 1
0.1%
1484 1
0.1%
1488 1
0.1%
1495 1
0.1%
1504 1
0.1%
1526 1
0.1%
1533 2
0.1%
1596 1
0.1%
ValueCountFrequency (%)
56600 1
0.1%
51974 1
0.1%
50102 1
0.1%
47280 1
0.1%
47007 1
0.1%
43500 1
0.1%
41600 1
0.1%
39384 1
0.1%
39290 1
0.1%
33983 1
0.1%

Street
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Pave
1453 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5836
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1453
99.6%
Grvl 6
 
0.4%

Length

2025-06-03T13:16:50.702248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:50.742682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pave 1453
99.6%
grvl 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 1459
25.0%
P 1453
24.9%
a 1453
24.9%
e 1453
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 1459
25.0%
P 1453
24.9%
a 1453
24.9%
e 1453
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 1459
25.0%
P 1453
24.9%
a 1453
24.9%
e 1453
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 1459
25.0%
P 1453
24.9%
a 1453
24.9%
e 1453
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Alley
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.9%
Missing1352
Missing (%)92.7%
Memory size11.5 KiB
Grvl
70 
Pave
37 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters428
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Grvl 70
 
4.8%
Pave 37
 
2.5%
(Missing) 1352
92.7%

Length

2025-06-03T13:16:50.785299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:50.823867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
grvl 70
65.4%
pave 37
34.6%

Most occurring characters

ValueCountFrequency (%)
v 107
25.0%
G 70
16.4%
r 70
16.4%
l 70
16.4%
P 37
 
8.6%
a 37
 
8.6%
e 37
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 107
25.0%
G 70
16.4%
r 70
16.4%
l 70
16.4%
P 37
 
8.6%
a 37
 
8.6%
e 37
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 107
25.0%
G 70
16.4%
r 70
16.4%
l 70
16.4%
P 37
 
8.6%
a 37
 
8.6%
e 37
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 107
25.0%
G 70
16.4%
r 70
16.4%
l 70
16.4%
P 37
 
8.6%
a 37
 
8.6%
e 37
 
8.6%

LotShape
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
934 
IR1
484 
IR2
 
35
IR3
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowIR1
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 934
64.0%
IR1 484
33.2%
IR2 35
 
2.4%
IR3 6
 
0.4%

Length

2025-06-03T13:16:50.866303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:50.910254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
reg 934
64.0%
ir1 484
33.2%
ir2 35
 
2.4%
ir3 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
R 1459
33.3%
e 934
21.3%
g 934
21.3%
I 525
 
12.0%
1 484
 
11.1%
2 35
 
0.8%
3 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 934
21.3%
g 934
21.3%
I 525
 
12.0%
1 484
 
11.1%
2 35
 
0.8%
3 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 934
21.3%
g 934
21.3%
I 525
 
12.0%
1 484
 
11.1%
2 35
 
0.8%
3 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 934
21.3%
g 934
21.3%
I 525
 
12.0%
1 484
 
11.1%
2 35
 
0.8%
3 6
 
0.1%

LandContour
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Lvl
1311 
HLS
 
70
Bnk
 
54
Low
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowHLS

Common Values

ValueCountFrequency (%)
Lvl 1311
89.9%
HLS 70
 
4.8%
Bnk 54
 
3.7%
Low 24
 
1.6%

Length

2025-06-03T13:16:50.957017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.000179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1311
89.9%
hls 70
 
4.8%
bnk 54
 
3.7%
low 24
 
1.6%

Most occurring characters

ValueCountFrequency (%)
L 1405
32.1%
v 1311
30.0%
l 1311
30.0%
H 70
 
1.6%
S 70
 
1.6%
B 54
 
1.2%
n 54
 
1.2%
k 54
 
1.2%
o 24
 
0.5%
w 24
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1405
32.1%
v 1311
30.0%
l 1311
30.0%
H 70
 
1.6%
S 70
 
1.6%
B 54
 
1.2%
n 54
 
1.2%
k 54
 
1.2%
o 24
 
0.5%
w 24
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1405
32.1%
v 1311
30.0%
l 1311
30.0%
H 70
 
1.6%
S 70
 
1.6%
B 54
 
1.2%
n 54
 
1.2%
k 54
 
1.2%
o 24
 
0.5%
w 24
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1405
32.1%
v 1311
30.0%
l 1311
30.0%
H 70
 
1.6%
S 70
 
1.6%
B 54
 
1.2%
n 54
 
1.2%
k 54
 
1.2%
o 24
 
0.5%
w 24
 
0.5%

Utilities
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size11.5 KiB
AllPub
1457 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8742
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1457
99.9%
(Missing) 2
 
0.1%

Length

2025-06-03T13:16:51.048630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.089466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1457
100.0%

Most occurring characters

ValueCountFrequency (%)
l 2914
33.3%
A 1457
16.7%
P 1457
16.7%
u 1457
16.7%
b 1457
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2914
33.3%
A 1457
16.7%
P 1457
16.7%
u 1457
16.7%
b 1457
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2914
33.3%
A 1457
16.7%
P 1457
16.7%
u 1457
16.7%
b 1457
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2914
33.3%
A 1457
16.7%
P 1457
16.7%
u 1457
16.7%
b 1457
16.7%

LotConfig
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Inside
1081 
Corner
248 
CulDSac
 
82
FR2
 
38
FR3
 
10

Length

Max length7
Median length6
Mean length5.9575051
Min length3

Characters and Unicode

Total characters8692
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowCorner
3rd rowInside
4th rowInside
5th rowInside

Common Values

ValueCountFrequency (%)
Inside 1081
74.1%
Corner 248
 
17.0%
CulDSac 82
 
5.6%
FR2 38
 
2.6%
FR3 10
 
0.7%

Length

2025-06-03T13:16:51.145033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.203226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
inside 1081
74.1%
corner 248
 
17.0%
culdsac 82
 
5.6%
fr2 38
 
2.6%
fr3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 1329
15.3%
n 1329
15.3%
I 1081
12.4%
s 1081
12.4%
i 1081
12.4%
d 1081
12.4%
r 496
 
5.7%
C 330
 
3.8%
o 248
 
2.9%
S 82
 
0.9%
Other values (9) 554
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1329
15.3%
n 1329
15.3%
I 1081
12.4%
s 1081
12.4%
i 1081
12.4%
d 1081
12.4%
r 496
 
5.7%
C 330
 
3.8%
o 248
 
2.9%
S 82
 
0.9%
Other values (9) 554
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1329
15.3%
n 1329
15.3%
I 1081
12.4%
s 1081
12.4%
i 1081
12.4%
d 1081
12.4%
r 496
 
5.7%
C 330
 
3.8%
o 248
 
2.9%
S 82
 
0.9%
Other values (9) 554
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1329
15.3%
n 1329
15.3%
I 1081
12.4%
s 1081
12.4%
i 1081
12.4%
d 1081
12.4%
r 496
 
5.7%
C 330
 
3.8%
o 248
 
2.9%
S 82
 
0.9%
Other values (9) 554
6.4%

LandSlope
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gtl
1396 
Mod
 
60
Sev
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1396
95.7%
Mod 60
 
4.1%
Sev 3
 
0.2%

Length

2025-06-03T13:16:51.262027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.305112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1396
95.7%
mod 60
 
4.1%
sev 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
G 1396
31.9%
t 1396
31.9%
l 1396
31.9%
M 60
 
1.4%
o 60
 
1.4%
d 60
 
1.4%
S 3
 
0.1%
e 3
 
0.1%
v 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1396
31.9%
t 1396
31.9%
l 1396
31.9%
M 60
 
1.4%
o 60
 
1.4%
d 60
 
1.4%
S 3
 
0.1%
e 3
 
0.1%
v 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1396
31.9%
t 1396
31.9%
l 1396
31.9%
M 60
 
1.4%
o 60
 
1.4%
d 60
 
1.4%
S 3
 
0.1%
e 3
 
0.1%
v 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1396
31.9%
t 1396
31.9%
l 1396
31.9%
M 60
 
1.4%
o 60
 
1.4%
d 60
 
1.4%
S 3
 
0.1%
e 3
 
0.1%
v 3
 
0.1%

Neighborhood
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
218 
OldTown
126 
CollgCr
117 
Somerst
96 
Edwards
94 
Other values (20)
808 

Length

Max length7
Median length7
Mean length6.5058259
Min length5

Characters and Unicode

Total characters9492
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNAmes
2nd rowNAmes
3rd rowGilbert
4th rowGilbert
5th rowStoneBr

Common Values

ValueCountFrequency (%)
NAmes 218
14.9%
OldTown 126
 
8.6%
CollgCr 117
 
8.0%
Somerst 96
 
6.6%
Edwards 94
 
6.4%
NridgHt 89
 
6.1%
Gilbert 86
 
5.9%
Sawyer 77
 
5.3%
SawyerW 66
 
4.5%
Mitchel 65
 
4.5%
Other values (15) 425
29.1%

Length

2025-06-03T13:16:51.361368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 218
14.9%
oldtown 126
 
8.6%
collgcr 117
 
8.0%
somerst 96
 
6.6%
edwards 94
 
6.4%
nridght 89
 
6.1%
gilbert 86
 
5.9%
sawyer 77
 
5.3%
sawyerw 66
 
4.5%
mitchel 65
 
4.5%
Other values (15) 425
29.1%

Most occurring characters

ValueCountFrequency (%)
e 911
 
9.6%
r 898
 
9.5%
l 588
 
6.2%
d 503
 
5.3%
s 474
 
5.0%
o 467
 
4.9%
w 435
 
4.6%
m 417
 
4.4%
N 409
 
4.3%
t 381
 
4.0%
Other values (28) 4009
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 911
 
9.6%
r 898
 
9.5%
l 588
 
6.2%
d 503
 
5.3%
s 474
 
5.0%
o 467
 
4.9%
w 435
 
4.6%
m 417
 
4.4%
N 409
 
4.3%
t 381
 
4.0%
Other values (28) 4009
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 911
 
9.6%
r 898
 
9.5%
l 588
 
6.2%
d 503
 
5.3%
s 474
 
5.0%
o 467
 
4.9%
w 435
 
4.6%
m 417
 
4.4%
N 409
 
4.3%
t 381
 
4.0%
Other values (28) 4009
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 911
 
9.6%
r 898
 
9.5%
l 588
 
6.2%
d 503
 
5.3%
s 474
 
5.0%
o 467
 
4.9%
w 435
 
4.6%
m 417
 
4.4%
N 409
 
4.3%
t 381
 
4.0%
Other values (28) 4009
42.2%

Condition1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1251 
Feedr
 
83
Artery
 
44
RRAn
 
24
PosN
 
20
Other values (4)
 
37

Length

Max length6
Median length4
Mean length4.1172036
Min length4

Characters and Unicode

Total characters6007
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeedr
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1251
85.7%
Feedr 83
 
5.7%
Artery 44
 
3.0%
RRAn 24
 
1.6%
PosN 20
 
1.4%
RRAe 17
 
1.2%
PosA 12
 
0.8%
RRNe 4
 
0.3%
RRNn 4
 
0.3%

Length

2025-06-03T13:16:51.421689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.484472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
norm 1251
85.7%
feedr 83
 
5.7%
artery 44
 
3.0%
rran 24
 
1.6%
posn 20
 
1.4%
rrae 17
 
1.2%
posa 12
 
0.8%
rrne 4
 
0.3%
rrnn 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 1422
23.7%
o 1283
21.4%
N 1279
21.3%
m 1251
20.8%
e 231
 
3.8%
R 98
 
1.6%
A 97
 
1.6%
F 83
 
1.4%
d 83
 
1.4%
t 44
 
0.7%
Other values (4) 136
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1422
23.7%
o 1283
21.4%
N 1279
21.3%
m 1251
20.8%
e 231
 
3.8%
R 98
 
1.6%
A 97
 
1.6%
F 83
 
1.4%
d 83
 
1.4%
t 44
 
0.7%
Other values (4) 136
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1422
23.7%
o 1283
21.4%
N 1279
21.3%
m 1251
20.8%
e 231
 
3.8%
R 98
 
1.6%
A 97
 
1.6%
F 83
 
1.4%
d 83
 
1.4%
t 44
 
0.7%
Other values (4) 136
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1422
23.7%
o 1283
21.4%
N 1279
21.3%
m 1251
20.8%
e 231
 
3.8%
R 98
 
1.6%
A 97
 
1.6%
F 83
 
1.4%
d 83
 
1.4%
t 44
 
0.7%
Other values (4) 136
 
2.3%

Condition2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1444 
Feedr
 
7
PosA
 
3
Artery
 
3
PosN
 
2

Length

Max length6
Median length4
Mean length4.0089102
Min length4

Characters and Unicode

Total characters5849
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1444
99.0%
Feedr 7
 
0.5%
PosA 3
 
0.2%
Artery 3
 
0.2%
PosN 2
 
0.1%

Length

2025-06-03T13:16:51.546138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.596438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
norm 1444
99.0%
feedr 7
 
0.5%
posa 3
 
0.2%
artery 3
 
0.2%
posn 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1457
24.9%
o 1449
24.8%
N 1446
24.7%
m 1444
24.7%
e 17
 
0.3%
F 7
 
0.1%
d 7
 
0.1%
A 6
 
0.1%
P 5
 
0.1%
s 5
 
0.1%
Other values (2) 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1457
24.9%
o 1449
24.8%
N 1446
24.7%
m 1444
24.7%
e 17
 
0.3%
F 7
 
0.1%
d 7
 
0.1%
A 6
 
0.1%
P 5
 
0.1%
s 5
 
0.1%
Other values (2) 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1457
24.9%
o 1449
24.8%
N 1446
24.7%
m 1444
24.7%
e 17
 
0.3%
F 7
 
0.1%
d 7
 
0.1%
A 6
 
0.1%
P 5
 
0.1%
s 5
 
0.1%
Other values (2) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1457
24.9%
o 1449
24.8%
N 1446
24.7%
m 1444
24.7%
e 17
 
0.3%
F 7
 
0.1%
d 7
 
0.1%
A 6
 
0.1%
P 5
 
0.1%
s 5
 
0.1%
Other values (2) 6
 
0.1%

BldgType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1205 
TwnhsE
 
113
Duplex
 
57
Twnhs
 
53
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.3118574
Min length4

Characters and Unicode

Total characters6291
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th rowTwnhsE

Common Values

ValueCountFrequency (%)
1Fam 1205
82.6%
TwnhsE 113
 
7.7%
Duplex 57
 
3.9%
Twnhs 53
 
3.6%
2fmCon 31
 
2.1%

Length

2025-06-03T13:16:51.649413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.698688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1205
82.6%
twnhse 113
 
7.7%
duplex 57
 
3.9%
twnhs 53
 
3.6%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1236
19.6%
1 1205
19.2%
a 1205
19.2%
F 1205
19.2%
n 197
 
3.1%
T 166
 
2.6%
w 166
 
2.6%
h 166
 
2.6%
s 166
 
2.6%
E 113
 
1.8%
Other values (10) 466
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1236
19.6%
1 1205
19.2%
a 1205
19.2%
F 1205
19.2%
n 197
 
3.1%
T 166
 
2.6%
w 166
 
2.6%
h 166
 
2.6%
s 166
 
2.6%
E 113
 
1.8%
Other values (10) 466
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1236
19.6%
1 1205
19.2%
a 1205
19.2%
F 1205
19.2%
n 197
 
3.1%
T 166
 
2.6%
w 166
 
2.6%
h 166
 
2.6%
s 166
 
2.6%
E 113
 
1.8%
Other values (10) 466
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1236
19.6%
1 1205
19.2%
a 1205
19.2%
F 1205
19.2%
n 197
 
3.1%
T 166
 
2.6%
w 166
 
2.6%
h 166
 
2.6%
s 166
 
2.6%
E 113
 
1.8%
Other values (10) 466
 
7.4%

HouseStyle
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
745 
2Story
427 
1.5Fin
160 
SLvl
 
63
SFoyer
 
46
Other values (2)
 
18

Length

Max length6
Median length6
Mean length5.9136395
Min length4

Characters and Unicode

Total characters8628
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Story
2nd row1Story
3rd row2Story
4th row2Story
5th row1Story

Common Values

ValueCountFrequency (%)
1Story 745
51.1%
2Story 427
29.3%
1.5Fin 160
 
11.0%
SLvl 63
 
4.3%
SFoyer 46
 
3.2%
2.5Unf 13
 
0.9%
1.5Unf 5
 
0.3%

Length

2025-06-03T13:16:51.752399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:51.883246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1story 745
51.1%
2story 427
29.3%
1.5fin 160
 
11.0%
slvl 63
 
4.3%
sfoyer 46
 
3.2%
2.5unf 13
 
0.9%
1.5unf 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 1281
14.8%
o 1218
14.1%
r 1218
14.1%
y 1218
14.1%
t 1172
13.6%
1 910
10.5%
2 440
 
5.1%
F 206
 
2.4%
5 178
 
2.1%
. 178
 
2.1%
Other values (8) 609
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1281
14.8%
o 1218
14.1%
r 1218
14.1%
y 1218
14.1%
t 1172
13.6%
1 910
10.5%
2 440
 
5.1%
F 206
 
2.4%
5 178
 
2.1%
. 178
 
2.1%
Other values (8) 609
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1281
14.8%
o 1218
14.1%
r 1218
14.1%
y 1218
14.1%
t 1172
13.6%
1 910
10.5%
2 440
 
5.1%
F 206
 
2.4%
5 178
 
2.1%
. 178
 
2.1%
Other values (8) 609
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1281
14.8%
o 1218
14.1%
r 1218
14.1%
y 1218
14.1%
t 1172
13.6%
1 910
10.5%
2 440
 
5.1%
F 206
 
2.4%
5 178
 
2.1%
. 178
 
2.1%
Other values (8) 609
7.1%

OverallQual
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0788211
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:51.931380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4368116
Coefficient of variation (CV)0.23636353
Kurtosis0.037640747
Mean6.0788211
Median Absolute Deviation (MAD)1
Skewness0.18119602
Sum8869
Variance2.0644277
MonotonicityNot monotonic
2025-06-03T13:16:51.972144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 428
29.3%
6 357
24.5%
7 281
19.3%
8 174
11.9%
4 110
 
7.5%
9 64
 
4.4%
3 20
 
1.4%
10 13
 
0.9%
2 10
 
0.7%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 10
 
0.7%
3 20
 
1.4%
4 110
 
7.5%
5 428
29.3%
6 357
24.5%
7 281
19.3%
8 174
11.9%
9 64
 
4.4%
10 13
 
0.9%
ValueCountFrequency (%)
10 13
 
0.9%
9 64
 
4.4%
8 174
11.9%
7 281
19.3%
6 357
24.5%
5 428
29.3%
4 110
 
7.5%
3 20
 
1.4%
2 10
 
0.7%
1 2
 
0.1%

OverallCond
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.553804
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:52.010320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1137396
Coefficient of variation (CV)0.20053635
Kurtosis1.8518186
Mean5.553804
Median Absolute Deviation (MAD)0
Skewness0.44916487
Sum8103
Variance1.2404159
MonotonicityNot monotonic
2025-06-03T13:16:52.054490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 824
56.5%
6 279
 
19.1%
7 185
 
12.7%
8 72
 
4.9%
4 44
 
3.0%
3 25
 
1.7%
9 19
 
1.3%
1 6
 
0.4%
2 5
 
0.3%
ValueCountFrequency (%)
1 6
 
0.4%
2 5
 
0.3%
3 25
 
1.7%
4 44
 
3.0%
5 824
56.5%
6 279
 
19.1%
7 185
 
12.7%
8 72
 
4.9%
9 19
 
1.3%
ValueCountFrequency (%)
9 19
 
1.3%
8 72
 
4.9%
7 185
 
12.7%
6 279
 
19.1%
5 824
56.5%
4 44
 
3.0%
3 25
 
1.7%
2 5
 
0.3%
1 6
 
0.4%

YearBuilt
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.3578
Minimum1879
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:52.108550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1879
5-th percentile1915
Q11953
median1973
Q32001
95-th percentile2007
Maximum2010
Range131
Interquartile range (IQR)48

Descriptive statistics

Standard deviation30.390071
Coefficient of variation (CV)0.015415807
Kurtosis-0.57932062
Mean1971.3578
Median Absolute Deviation (MAD)25
Skewness-0.58765661
Sum2876211
Variance923.55641
MonotonicityNot monotonic
2025-06-03T13:16:52.169197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 78
 
5.3%
2006 71
 
4.9%
2007 60
 
4.1%
2004 45
 
3.1%
2003 43
 
2.9%
1920 27
 
1.9%
1999 27
 
1.9%
1910 26
 
1.8%
2008 26
 
1.8%
1956 25
 
1.7%
Other values (96) 1031
70.7%
ValueCountFrequency (%)
1879 1
 
0.1%
1880 1
 
0.1%
1890 5
 
0.3%
1895 3
 
0.2%
1896 1
 
0.1%
1900 19
1.3%
1901 2
 
0.1%
1902 1
 
0.1%
1905 2
 
0.1%
1907 1
 
0.1%
ValueCountFrequency (%)
2010 2
 
0.1%
2009 7
 
0.5%
2008 26
 
1.8%
2007 60
4.1%
2006 71
4.9%
2005 78
5.3%
2004 45
3.1%
2003 43
2.9%
2002 24
 
1.6%
2001 15
 
1.0%

YearRemodAdd
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1983.6628
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:52.225286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11963
median1992
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)41

Descriptive statistics

Standard deviation21.130467
Coefficient of variation (CV)0.010652247
Kurtosis-1.4125857
Mean1983.6628
Median Absolute Deviation (MAD)15
Skewness-0.39990599
Sum2894164
Variance446.49663
MonotonicityNot monotonic
2025-06-03T13:16:52.281858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 183
 
12.5%
2006 105
 
7.2%
2007 88
 
6.0%
2005 68
 
4.7%
2000 49
 
3.4%
2004 49
 
3.4%
2003 48
 
3.3%
2008 41
 
2.8%
1998 41
 
2.8%
2002 34
 
2.3%
Other values (51) 753
51.6%
ValueCountFrequency (%)
1950 183
12.5%
1951 10
 
0.7%
1952 10
 
0.7%
1953 10
 
0.7%
1954 14
 
1.0%
1955 16
 
1.1%
1956 20
 
1.4%
1957 11
 
0.8%
1958 19
 
1.3%
1959 12
 
0.8%
ValueCountFrequency (%)
2010 7
 
0.5%
2009 11
 
0.8%
2008 41
 
2.8%
2007 88
6.0%
2006 105
7.2%
2005 68
4.7%
2004 49
3.4%
2003 48
3.3%
2002 34
 
2.3%
2001 28
 
1.9%

RoofStyle
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1169 
Hip
265 
Gambrel
 
11
Flat
 
7
Mansard
 
4

Length

Max length7
Median length5
Mean length4.6504455
Min length3

Characters and Unicode

Total characters6785
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowHip
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1169
80.1%
Hip 265
 
18.2%
Gambrel 11
 
0.8%
Flat 7
 
0.5%
Mansard 4
 
0.3%
Shed 3
 
0.2%

Length

2025-06-03T13:16:52.340384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:52.391184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gable 1169
80.1%
hip 265
 
18.2%
gambrel 11
 
0.8%
flat 7
 
0.5%
mansard 4
 
0.3%
shed 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 1195
17.6%
l 1187
17.5%
e 1183
17.4%
G 1180
17.4%
b 1180
17.4%
H 265
 
3.9%
i 265
 
3.9%
p 265
 
3.9%
r 15
 
0.2%
m 11
 
0.2%
Other values (8) 39
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1195
17.6%
l 1187
17.5%
e 1183
17.4%
G 1180
17.4%
b 1180
17.4%
H 265
 
3.9%
i 265
 
3.9%
p 265
 
3.9%
r 15
 
0.2%
m 11
 
0.2%
Other values (8) 39
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1195
17.6%
l 1187
17.5%
e 1183
17.4%
G 1180
17.4%
b 1180
17.4%
H 265
 
3.9%
i 265
 
3.9%
p 265
 
3.9%
r 15
 
0.2%
m 11
 
0.2%
Other values (8) 39
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1195
17.6%
l 1187
17.5%
e 1183
17.4%
G 1180
17.4%
b 1180
17.4%
H 265
 
3.9%
i 265
 
3.9%
p 265
 
3.9%
r 15
 
0.2%
m 11
 
0.2%
Other values (8) 39
 
0.6%

RoofMatl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
CompShg
1442 
Tar&Grv
 
12
WdShake
 
4
WdShngl
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10213
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1442
98.8%
Tar&Grv 12
 
0.8%
WdShake 4
 
0.3%
WdShngl 1
 
0.1%

Length

2025-06-03T13:16:52.441541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:52.501535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1442
98.8%
tar&grv 12
 
0.8%
wdshake 4
 
0.3%
wdshngl 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1447
14.2%
h 1447
14.2%
g 1443
14.1%
C 1442
14.1%
o 1442
14.1%
m 1442
14.1%
p 1442
14.1%
r 24
 
0.2%
a 16
 
0.2%
T 12
 
0.1%
Other values (9) 56
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1447
14.2%
h 1447
14.2%
g 1443
14.1%
C 1442
14.1%
o 1442
14.1%
m 1442
14.1%
p 1442
14.1%
r 24
 
0.2%
a 16
 
0.2%
T 12
 
0.1%
Other values (9) 56
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1447
14.2%
h 1447
14.2%
g 1443
14.1%
C 1442
14.1%
o 1442
14.1%
m 1442
14.1%
p 1442
14.1%
r 24
 
0.2%
a 16
 
0.2%
T 12
 
0.1%
Other values (9) 56
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1447
14.2%
h 1447
14.2%
g 1443
14.1%
C 1442
14.1%
o 1442
14.1%
m 1442
14.1%
p 1442
14.1%
r 24
 
0.2%
a 16
 
0.2%
T 12
 
0.1%
Other values (9) 56
 
0.5%

Exterior1st
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.9%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
VinylSd
510 
MetalSd
230 
HdBoard
220 
Wd Sdng
205 
Plywood
113 
Other values (8)
180 

Length

Max length7
Median length7
Mean length6.9869684
Min length6

Characters and Unicode

Total characters10187
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowWd Sdng
3rd rowVinylSd
4th rowVinylSd
5th rowHdBoard

Common Values

ValueCountFrequency (%)
VinylSd 510
35.0%
MetalSd 230
15.8%
HdBoard 220
15.1%
Wd Sdng 205
14.1%
Plywood 113
 
7.7%
CemntBd 65
 
4.5%
BrkFace 37
 
2.5%
WdShing 30
 
2.1%
AsbShng 24
 
1.6%
Stucco 18
 
1.2%
Other values (3) 6
 
0.4%

Length

2025-06-03T13:16:52.550418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 510
30.7%
metalsd 230
13.8%
hdboard 220
13.2%
wd 205
12.3%
sdng 205
12.3%
plywood 113
 
6.8%
cemntbd 65
 
3.9%
brkface 37
 
2.2%
wdshing 30
 
1.8%
asbshng 24
 
1.4%
Other values (4) 24
 
1.4%

Most occurring characters

ValueCountFrequency (%)
d 1798
17.6%
S 1018
 
10.0%
l 854
 
8.4%
n 835
 
8.2%
y 623
 
6.1%
i 540
 
5.3%
V 510
 
5.0%
a 487
 
4.8%
o 469
 
4.6%
e 332
 
3.3%
Other values (21) 2721
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1798
17.6%
S 1018
 
10.0%
l 854
 
8.4%
n 835
 
8.2%
y 623
 
6.1%
i 540
 
5.3%
V 510
 
5.0%
a 487
 
4.8%
o 469
 
4.6%
e 332
 
3.3%
Other values (21) 2721
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1798
17.6%
S 1018
 
10.0%
l 854
 
8.4%
n 835
 
8.2%
y 623
 
6.1%
i 540
 
5.3%
V 510
 
5.0%
a 487
 
4.8%
o 469
 
4.6%
e 332
 
3.3%
Other values (21) 2721
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1798
17.6%
S 1018
 
10.0%
l 854
 
8.4%
n 835
 
8.2%
y 623
 
6.1%
i 540
 
5.3%
V 510
 
5.0%
a 487
 
4.8%
o 469
 
4.6%
e 332
 
3.3%
Other values (21) 2721
26.7%

Exterior2nd
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
VinylSd
510 
MetalSd
233 
HdBoard
199 
Wd Sdng
194 
Plywood
128 
Other values (10)
194 

Length

Max length7
Median length7
Mean length6.9828532
Min length5

Characters and Unicode

Total characters10181
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowWd Sdng
3rd rowVinylSd
4th rowVinylSd
5th rowHdBoard

Common Values

ValueCountFrequency (%)
VinylSd 510
35.0%
MetalSd 233
16.0%
HdBoard 199
 
13.6%
Wd Sdng 194
 
13.3%
Plywood 128
 
8.8%
CmentBd 66
 
4.5%
Wd Shng 43
 
2.9%
BrkFace 22
 
1.5%
Stucco 21
 
1.4%
AsbShng 18
 
1.2%
Other values (5) 24
 
1.6%

Length

2025-06-03T13:16:52.604781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 510
29.8%
wd 237
13.9%
metalsd 233
13.6%
hdboard 199
 
11.6%
sdng 194
 
11.3%
plywood 128
 
7.5%
cmentbd 66
 
3.9%
shng 43
 
2.5%
brkface 22
 
1.3%
stucco 21
 
1.2%
Other values (7) 57
 
3.3%

Most occurring characters

ValueCountFrequency (%)
d 1766
17.3%
S 1026
 
10.1%
l 873
 
8.6%
n 848
 
8.3%
y 638
 
6.3%
V 510
 
5.0%
i 510
 
5.0%
o 479
 
4.7%
a 454
 
4.5%
t 326
 
3.2%
Other values (22) 2751
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1026
 
10.1%
l 873
 
8.6%
n 848
 
8.3%
y 638
 
6.3%
V 510
 
5.0%
i 510
 
5.0%
o 479
 
4.7%
a 454
 
4.5%
t 326
 
3.2%
Other values (22) 2751
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1026
 
10.1%
l 873
 
8.6%
n 848
 
8.3%
y 638
 
6.3%
V 510
 
5.0%
i 510
 
5.0%
o 479
 
4.7%
a 454
 
4.5%
t 326
 
3.2%
Other values (22) 2751
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1026
 
10.1%
l 873
 
8.6%
n 848
 
8.3%
y 638
 
6.3%
V 510
 
5.0%
i 510
 
5.0%
o 479
 
4.7%
a 454
 
4.5%
t 326
 
3.2%
Other values (22) 2751
27.0%

MasVnrType
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.5%
Missing894
Missing (%)61.3%
Memory size11.5 KiB
BrkFace
434 
Stone
121 
BrkCmn
 
10

Length

Max length7
Median length7
Mean length6.5539823
Min length5

Characters and Unicode

Total characters3703
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowBrkFace
5th rowStone

Common Values

ValueCountFrequency (%)
BrkFace 434
29.7%
Stone 121
 
8.3%
BrkCmn 10
 
0.7%
(Missing) 894
61.3%

Length

2025-06-03T13:16:52.660112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:52.704478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
brkface 434
76.8%
stone 121
 
21.4%
brkcmn 10
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 555
15.0%
B 444
12.0%
r 444
12.0%
k 444
12.0%
F 434
11.7%
a 434
11.7%
c 434
11.7%
n 131
 
3.5%
S 121
 
3.3%
t 121
 
3.3%
Other values (3) 141
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 555
15.0%
B 444
12.0%
r 444
12.0%
k 444
12.0%
F 434
11.7%
a 434
11.7%
c 434
11.7%
n 131
 
3.5%
S 121
 
3.3%
t 121
 
3.3%
Other values (3) 141
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 555
15.0%
B 444
12.0%
r 444
12.0%
k 444
12.0%
F 434
11.7%
a 434
11.7%
c 434
11.7%
n 131
 
3.5%
S 121
 
3.3%
t 121
 
3.3%
Other values (3) 141
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 555
15.0%
B 444
12.0%
r 444
12.0%
k 444
12.0%
F 434
11.7%
a 434
11.7%
c 434
11.7%
n 131
 
3.5%
S 121
 
3.3%
t 121
 
3.3%
Other values (3) 141
 
3.8%

MasVnrArea
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct303
Distinct (%)21.0%
Missing15
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean100.70914
Minimum0
Maximum1290
Zeros877
Zeros (%)60.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:52.753589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164
95-th percentile478.95
Maximum1290
Range1290
Interquartile range (IQR)164

Descriptive statistics

Standard deviation177.6259
Coefficient of variation (CV)1.7637515
Kurtosis8.3763083
Mean100.70914
Median Absolute Deviation (MAD)0
Skewness2.5333767
Sum145424
Variance31550.96
MonotonicityNot monotonic
2025-06-03T13:16:52.814723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 877
60.1%
176 10
 
0.7%
144 9
 
0.6%
120 8
 
0.5%
216 8
 
0.5%
200 7
 
0.5%
504 6
 
0.4%
198 6
 
0.4%
128 6
 
0.4%
302 6
 
0.4%
Other values (293) 501
34.3%
(Missing) 15
 
1.0%
ValueCountFrequency (%)
0 877
60.1%
1 1
 
0.1%
3 1
 
0.1%
14 3
 
0.2%
16 4
 
0.3%
18 1
 
0.1%
20 4
 
0.3%
22 1
 
0.1%
23 4
 
0.3%
24 1
 
0.1%
ValueCountFrequency (%)
1290 1
0.1%
1224 2
0.1%
1159 1
0.1%
1110 1
0.1%
1095 1
0.1%
1050 1
0.1%
970 1
0.1%
945 1
0.1%
902 1
0.1%
886 1
0.1%

ExterQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
892 
Gd
491 
Ex
 
55
Fa
 
21

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 892
61.1%
Gd 491
33.7%
Ex 55
 
3.8%
Fa 21
 
1.4%

Length

2025-06-03T13:16:52.867363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:52.909872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 892
61.1%
gd 491
33.7%
ex 55
 
3.8%
fa 21
 
1.4%

Most occurring characters

ValueCountFrequency (%)
T 892
30.6%
A 892
30.6%
G 491
16.8%
d 491
16.8%
E 55
 
1.9%
x 55
 
1.9%
F 21
 
0.7%
a 21
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 892
30.6%
A 892
30.6%
G 491
16.8%
d 491
16.8%
E 55
 
1.9%
x 55
 
1.9%
F 21
 
0.7%
a 21
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 892
30.6%
A 892
30.6%
G 491
16.8%
d 491
16.8%
E 55
 
1.9%
x 55
 
1.9%
F 21
 
0.7%
a 21
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 892
30.6%
A 892
30.6%
G 491
16.8%
d 491
16.8%
E 55
 
1.9%
x 55
 
1.9%
F 21
 
0.7%
a 21
 
0.7%

ExterCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1256 
Gd
153 
Fa
 
39
Ex
 
9
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1256
86.1%
Gd 153
 
10.5%
Fa 39
 
2.7%
Ex 9
 
0.6%
Po 2
 
0.1%

Length

2025-06-03T13:16:52.956853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.001663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1256
86.1%
gd 153
 
10.5%
fa 39
 
2.7%
ex 9
 
0.6%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1256
43.0%
A 1256
43.0%
G 153
 
5.2%
d 153
 
5.2%
F 39
 
1.3%
a 39
 
1.3%
E 9
 
0.3%
x 9
 
0.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1256
43.0%
A 1256
43.0%
G 153
 
5.2%
d 153
 
5.2%
F 39
 
1.3%
a 39
 
1.3%
E 9
 
0.3%
x 9
 
0.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1256
43.0%
A 1256
43.0%
G 153
 
5.2%
d 153
 
5.2%
F 39
 
1.3%
a 39
 
1.3%
E 9
 
0.3%
x 9
 
0.3%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1256
43.0%
A 1256
43.0%
G 153
 
5.2%
d 153
 
5.2%
F 39
 
1.3%
a 39
 
1.3%
E 9
 
0.3%
x 9
 
0.3%
P 2
 
0.1%
o 2
 
0.1%

Foundation
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
661 
CBlock
601 
BrkTil
165 
Slab
 
25
Stone
 
5

Length

Max length6
Median length6
Mean length5.5065113
Min length4

Characters and Unicode

Total characters8034
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCBlock
2nd rowCBlock
3rd rowPConc
4th rowPConc
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 661
45.3%
CBlock 601
41.2%
BrkTil 165
 
11.3%
Slab 25
 
1.7%
Stone 5
 
0.3%
Wood 2
 
0.1%

Length

2025-06-03T13:16:53.055490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.125539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pconc 661
45.3%
cblock 601
41.2%
brktil 165
 
11.3%
slab 25
 
1.7%
stone 5
 
0.3%
wood 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1271
15.8%
C 1262
15.7%
c 1262
15.7%
l 791
9.8%
B 766
9.5%
k 766
9.5%
n 666
8.3%
P 661
8.2%
i 165
 
2.1%
T 165
 
2.1%
Other values (8) 259
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1271
15.8%
C 1262
15.7%
c 1262
15.7%
l 791
9.8%
B 766
9.5%
k 766
9.5%
n 666
8.3%
P 661
8.2%
i 165
 
2.1%
T 165
 
2.1%
Other values (8) 259
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1271
15.8%
C 1262
15.7%
c 1262
15.7%
l 791
9.8%
B 766
9.5%
k 766
9.5%
n 666
8.3%
P 661
8.2%
i 165
 
2.1%
T 165
 
2.1%
Other values (8) 259
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1271
15.8%
C 1262
15.7%
c 1262
15.7%
l 791
9.8%
B 766
9.5%
k 766
9.5%
n 666
8.3%
P 661
8.2%
i 165
 
2.1%
T 165
 
2.1%
Other values (8) 259
 
3.2%

BsmtQual
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.3%
Missing44
Missing (%)3.0%
Memory size11.5 KiB
TA
634 
Gd
591 
Ex
137 
Fa
 
53

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2830
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 634
43.5%
Gd 591
40.5%
Ex 137
 
9.4%
Fa 53
 
3.6%
(Missing) 44
 
3.0%

Length

2025-06-03T13:16:53.186924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.230378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 634
44.8%
gd 591
41.8%
ex 137
 
9.7%
fa 53
 
3.7%

Most occurring characters

ValueCountFrequency (%)
T 634
22.4%
A 634
22.4%
G 591
20.9%
d 591
20.9%
E 137
 
4.8%
x 137
 
4.8%
F 53
 
1.9%
a 53
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 634
22.4%
A 634
22.4%
G 591
20.9%
d 591
20.9%
E 137
 
4.8%
x 137
 
4.8%
F 53
 
1.9%
a 53
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 634
22.4%
A 634
22.4%
G 591
20.9%
d 591
20.9%
E 137
 
4.8%
x 137
 
4.8%
F 53
 
1.9%
a 53
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 634
22.4%
A 634
22.4%
G 591
20.9%
d 591
20.9%
E 137
 
4.8%
x 137
 
4.8%
F 53
 
1.9%
a 53
 
1.9%

BsmtCond
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.3%
Missing45
Missing (%)3.1%
Memory size11.5 KiB
TA
1295 
Fa
 
59
Gd
 
57
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2828
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1295
88.8%
Fa 59
 
4.0%
Gd 57
 
3.9%
Po 3
 
0.2%
(Missing) 45
 
3.1%

Length

2025-06-03T13:16:53.280631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.327463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1295
91.6%
fa 59
 
4.2%
gd 57
 
4.0%
po 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 1295
45.8%
A 1295
45.8%
F 59
 
2.1%
a 59
 
2.1%
G 57
 
2.0%
d 57
 
2.0%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1295
45.8%
A 1295
45.8%
F 59
 
2.1%
a 59
 
2.1%
G 57
 
2.0%
d 57
 
2.0%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1295
45.8%
A 1295
45.8%
F 59
 
2.1%
a 59
 
2.1%
G 57
 
2.0%
d 57
 
2.0%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1295
45.8%
A 1295
45.8%
F 59
 
2.1%
a 59
 
2.1%
G 57
 
2.0%
d 57
 
2.0%
P 3
 
0.1%
o 3
 
0.1%

BsmtExposure
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.3%
Missing44
Missing (%)3.0%
Memory size11.5 KiB
No
951 
Av
197 
Gd
142 
Mn
125 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2830
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 951
65.2%
Av 197
 
13.5%
Gd 142
 
9.7%
Mn 125
 
8.6%
(Missing) 44
 
3.0%

Length

2025-06-03T13:16:53.376092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.422765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
no 951
67.2%
av 197
 
13.9%
gd 142
 
10.0%
mn 125
 
8.8%

Most occurring characters

ValueCountFrequency (%)
N 951
33.6%
o 951
33.6%
A 197
 
7.0%
v 197
 
7.0%
G 142
 
5.0%
d 142
 
5.0%
M 125
 
4.4%
n 125
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 951
33.6%
o 951
33.6%
A 197
 
7.0%
v 197
 
7.0%
G 142
 
5.0%
d 142
 
5.0%
M 125
 
4.4%
n 125
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 951
33.6%
o 951
33.6%
A 197
 
7.0%
v 197
 
7.0%
G 142
 
5.0%
d 142
 
5.0%
M 125
 
4.4%
n 125
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 951
33.6%
o 951
33.6%
A 197
 
7.0%
v 197
 
7.0%
G 142
 
5.0%
d 142
 
5.0%
M 125
 
4.4%
n 125
 
4.4%

BsmtFinType1
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.4%
Missing42
Missing (%)2.9%
Memory size11.5 KiB
GLQ
431 
Unf
421 
ALQ
209 
Rec
155 
BLQ
121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4251
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRec
2nd rowALQ
3rd rowGLQ
4th rowGLQ
5th rowALQ

Common Values

ValueCountFrequency (%)
GLQ 431
29.5%
Unf 421
28.9%
ALQ 209
14.3%
Rec 155
 
10.6%
BLQ 121
 
8.3%
LwQ 80
 
5.5%
(Missing) 42
 
2.9%

Length

2025-06-03T13:16:53.475517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.527480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
glq 431
30.4%
unf 421
29.7%
alq 209
14.7%
rec 155
 
10.9%
blq 121
 
8.5%
lwq 80
 
5.6%

Most occurring characters

ValueCountFrequency (%)
L 841
19.8%
Q 841
19.8%
G 431
10.1%
U 421
9.9%
n 421
9.9%
f 421
9.9%
A 209
 
4.9%
R 155
 
3.6%
e 155
 
3.6%
c 155
 
3.6%
Other values (2) 201
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 841
19.8%
Q 841
19.8%
G 431
10.1%
U 421
9.9%
n 421
9.9%
f 421
9.9%
A 209
 
4.9%
R 155
 
3.6%
e 155
 
3.6%
c 155
 
3.6%
Other values (2) 201
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 841
19.8%
Q 841
19.8%
G 431
10.1%
U 421
9.9%
n 421
9.9%
f 421
9.9%
A 209
 
4.9%
R 155
 
3.6%
e 155
 
3.6%
c 155
 
3.6%
Other values (2) 201
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 841
19.8%
Q 841
19.8%
G 431
10.1%
U 421
9.9%
n 421
9.9%
f 421
9.9%
A 209
 
4.9%
R 155
 
3.6%
e 155
 
3.6%
c 155
 
3.6%
Other values (2) 201
 
4.7%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct669
Distinct (%)45.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean439.2037
Minimum0
Maximum4010
Zeros462
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:53.592809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median350.5
Q3753.5
95-th percentile1290.6
Maximum4010
Range4010
Interquartile range (IQR)753.5

Descriptive statistics

Standard deviation455.26804
Coefficient of variation (CV)1.0365761
Kurtosis2.6729662
Mean439.2037
Median Absolute Deviation (MAD)350.5
Skewness1.1656767
Sum640359
Variance207268.99
MonotonicityNot monotonic
2025-06-03T13:16:53.658790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
31.7%
24 15
 
1.0%
276 6
 
0.4%
602 6
 
0.4%
300 5
 
0.3%
288 5
 
0.3%
16 5
 
0.3%
758 5
 
0.3%
500 4
 
0.3%
456 4
 
0.3%
Other values (659) 941
64.5%
ValueCountFrequency (%)
0 462
31.7%
16 5
 
0.3%
20 3
 
0.2%
24 15
 
1.0%
28 2
 
0.1%
32 1
 
0.1%
36 3
 
0.2%
40 2
 
0.1%
42 1
 
0.1%
48 2
 
0.1%
ValueCountFrequency (%)
4010 1
0.1%
2288 1
0.1%
2257 1
0.1%
2158 1
0.1%
2146 1
0.1%
2085 1
0.1%
1972 1
0.1%
1965 1
0.1%
1836 1
0.1%
1812 1
0.1%

BsmtFinType2
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)0.4%
Missing42
Missing (%)2.9%
Memory size11.5 KiB
Unf
1237 
Rec
 
51
LwQ
 
41
BLQ
 
35
ALQ
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4251
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLwQ
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1237
84.8%
Rec 51
 
3.5%
LwQ 41
 
2.8%
BLQ 35
 
2.4%
ALQ 33
 
2.3%
GLQ 20
 
1.4%
(Missing) 42
 
2.9%

Length

2025-06-03T13:16:53.722185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:53.769398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unf 1237
87.3%
rec 51
 
3.6%
lwq 41
 
2.9%
blq 35
 
2.5%
alq 33
 
2.3%
glq 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
U 1237
29.1%
n 1237
29.1%
f 1237
29.1%
L 129
 
3.0%
Q 129
 
3.0%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 41
 
1.0%
B 35
 
0.8%
Other values (2) 53
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1237
29.1%
n 1237
29.1%
f 1237
29.1%
L 129
 
3.0%
Q 129
 
3.0%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 41
 
1.0%
B 35
 
0.8%
Other values (2) 53
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1237
29.1%
n 1237
29.1%
f 1237
29.1%
L 129
 
3.0%
Q 129
 
3.0%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 41
 
1.0%
B 35
 
0.8%
Other values (2) 53
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1237
29.1%
n 1237
29.1%
f 1237
29.1%
L 129
 
3.0%
Q 129
 
3.0%
R 51
 
1.2%
e 51
 
1.2%
c 51
 
1.2%
w 41
 
1.0%
B 35
 
0.8%
Other values (2) 53
 
1.2%

BsmtFinSF2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct161
Distinct (%)11.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean52.619342
Minimum0
Maximum1526
Zeros1278
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:53.824057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile448.15
Maximum1526
Range1526
Interquartile range (IQR)0

Descriptive statistics

Standard deviation176.75393
Coefficient of variation (CV)3.3591056
Kurtosis17.66723
Mean52.619342
Median Absolute Deviation (MAD)0
Skewness4.0413446
Sum76719
Variance31241.95
MonotonicityNot monotonic
2025-06-03T13:16:53.884843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1278
87.6%
483 3
 
0.2%
162 3
 
0.2%
294 3
 
0.2%
144 2
 
0.1%
252 2
 
0.1%
435 2
 
0.1%
60 2
 
0.1%
247 2
 
0.1%
116 2
 
0.1%
Other values (151) 159
 
10.9%
ValueCountFrequency (%)
0 1278
87.6%
6 1
 
0.1%
12 1
 
0.1%
38 1
 
0.1%
40 1
 
0.1%
42 2
 
0.1%
46 1
 
0.1%
48 1
 
0.1%
52 1
 
0.1%
60 2
 
0.1%
ValueCountFrequency (%)
1526 1
0.1%
1393 1
0.1%
1164 1
0.1%
1083 1
0.1%
1073 1
0.1%
1039 1
0.1%
1037 1
0.1%
1020 1
0.1%
982 1
0.1%
981 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct793
Distinct (%)54.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean554.29492
Minimum0
Maximum2140
Zeros123
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:53.945427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1219.25
median460
Q3797.75
95-th percentile1488.45
Maximum2140
Range2140
Interquartile range (IQR)578.5

Descriptive statistics

Standard deviation437.26049
Coefficient of variation (CV)0.7888589
Kurtosis0.33253511
Mean554.29492
Median Absolute Deviation (MAD)276.5
Skewness0.91991634
Sum808162
Variance191196.73
MonotonicityNot monotonic
2025-06-03T13:16:54.083422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
8.4%
384 11
 
0.8%
624 8
 
0.5%
348 7
 
0.5%
480 7
 
0.5%
672 7
 
0.5%
738 7
 
0.5%
100 7
 
0.5%
216 6
 
0.4%
120 6
 
0.4%
Other values (783) 1269
87.0%
ValueCountFrequency (%)
0 123
8.4%
17 1
 
0.1%
20 1
 
0.1%
22 1
 
0.1%
25 3
 
0.2%
27 1
 
0.1%
28 1
 
0.1%
30 5
 
0.3%
33 1
 
0.1%
34 1
 
0.1%
ValueCountFrequency (%)
2140 1
0.1%
2062 1
0.1%
1967 1
0.1%
1958 1
0.1%
1921 1
0.1%
1866 1
0.1%
1851 1
0.1%
1836 1
0.1%
1824 2
0.1%
1802 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct736
Distinct (%)50.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1046.118
Minimum0
Maximum5095
Zeros41
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:54.140075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile392
Q1784
median988
Q31305
95-th percentile1782
Maximum5095
Range5095
Interquartile range (IQR)521

Descriptive statistics

Standard deviation442.89862
Coefficient of variation (CV)0.4233735
Kurtosis5.2037805
Mean1046.118
Median Absolute Deviation (MAD)244
Skewness0.81358915
Sum1525240
Variance196159.19
MonotonicityNot monotonic
2025-06-03T13:16:54.198368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
2.8%
864 39
 
2.7%
960 13
 
0.9%
546 12
 
0.8%
768 12
 
0.8%
384 12
 
0.8%
672 12
 
0.8%
1008 12
 
0.8%
912 11
 
0.8%
1040 11
 
0.8%
Other values (726) 1283
87.9%
ValueCountFrequency (%)
0 41
2.8%
160 1
 
0.1%
173 1
 
0.1%
192 1
 
0.1%
216 2
 
0.1%
240 1
 
0.1%
245 1
 
0.1%
264 1
 
0.1%
279 1
 
0.1%
297 1
 
0.1%
ValueCountFrequency (%)
5095 1
0.1%
2846 1
0.1%
2660 1
0.1%
2630 1
0.1%
2552 1
0.1%
2535 1
0.1%
2492 1
0.1%
2461 1
0.1%
2458 1
0.1%
2452 1
0.1%

Heating
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GasA
1446 
GasW
 
9
Grav
 
2
Wall
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5836
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1446
99.1%
GasW 9
 
0.6%
Grav 2
 
0.1%
Wall 2
 
0.1%

Length

2025-06-03T13:16:54.250258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:54.293750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1446
99.1%
gasw 9
 
0.6%
grav 2
 
0.1%
wall 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1459
25.0%
G 1457
25.0%
s 1455
24.9%
A 1446
24.8%
W 11
 
0.2%
l 4
 
0.1%
r 2
 
< 0.1%
v 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1459
25.0%
G 1457
25.0%
s 1455
24.9%
A 1446
24.8%
W 11
 
0.2%
l 4
 
0.1%
r 2
 
< 0.1%
v 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1459
25.0%
G 1457
25.0%
s 1455
24.9%
A 1446
24.8%
W 11
 
0.2%
l 4
 
0.1%
r 2
 
< 0.1%
v 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1459
25.0%
G 1457
25.0%
s 1455
24.9%
A 1446
24.8%
W 11
 
0.2%
l 4
 
0.1%
r 2
 
< 0.1%
v 2
 
< 0.1%

HeatingQC
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
752 
TA
429 
Gd
233 
Fa
 
43
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowEx
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 752
51.5%
TA 429
29.4%
Gd 233
 
16.0%
Fa 43
 
2.9%
Po 2
 
0.1%

Length

2025-06-03T13:16:54.339944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:54.384829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ex 752
51.5%
ta 429
29.4%
gd 233
 
16.0%
fa 43
 
2.9%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 752
25.8%
x 752
25.8%
T 429
14.7%
A 429
14.7%
G 233
 
8.0%
d 233
 
8.0%
F 43
 
1.5%
a 43
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 752
25.8%
x 752
25.8%
T 429
14.7%
A 429
14.7%
G 233
 
8.0%
d 233
 
8.0%
F 43
 
1.5%
a 43
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 752
25.8%
x 752
25.8%
T 429
14.7%
A 429
14.7%
G 233
 
8.0%
d 233
 
8.0%
F 43
 
1.5%
a 43
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 752
25.8%
x 752
25.8%
T 429
14.7%
A 429
14.7%
G 233
 
8.0%
d 233
 
8.0%
F 43
 
1.5%
a 43
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

CentralAir
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1358 
False
 
101
ValueCountFrequency (%)
True 1358
93.1%
False 101
 
6.9%
2025-06-03T13:16:54.428216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Electrical
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
SBrkr
1337 
FuseA
 
94
FuseF
 
23
FuseP
 
5

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7295
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1337
91.6%
FuseA 94
 
6.4%
FuseF 23
 
1.6%
FuseP 5
 
0.3%

Length

2025-06-03T13:16:54.471717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:54.514043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1337
91.6%
fusea 94
 
6.4%
fusef 23
 
1.6%
fusep 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 2674
36.7%
S 1337
18.3%
B 1337
18.3%
k 1337
18.3%
F 145
 
2.0%
u 122
 
1.7%
s 122
 
1.7%
e 122
 
1.7%
A 94
 
1.3%
P 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2674
36.7%
S 1337
18.3%
B 1337
18.3%
k 1337
18.3%
F 145
 
2.0%
u 122
 
1.7%
s 122
 
1.7%
e 122
 
1.7%
A 94
 
1.3%
P 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2674
36.7%
S 1337
18.3%
B 1337
18.3%
k 1337
18.3%
F 145
 
2.0%
u 122
 
1.7%
s 122
 
1.7%
e 122
 
1.7%
A 94
 
1.3%
P 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2674
36.7%
S 1337
18.3%
B 1337
18.3%
k 1337
18.3%
F 145
 
2.0%
u 122
 
1.7%
s 122
 
1.7%
e 122
 
1.7%
A 94
 
1.3%
P 5
 
0.1%

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct789
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1156.5346
Minimum407
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:54.564276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum407
5-th percentile630
Q1873.5
median1079
Q31382.5
95-th percentile1829.1
Maximum5095
Range4688
Interquartile range (IQR)509

Descriptive statistics

Standard deviation398.16582
Coefficient of variation (CV)0.34427488
Kurtosis8.0538633
Mean1156.5346
Median Absolute Deviation (MAD)235
Skewness1.5581946
Sum1687384
Variance158536.02
MonotonicityNot monotonic
2025-06-03T13:16:54.622311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 21
 
1.4%
1040 12
 
0.8%
546 12
 
0.8%
960 11
 
0.8%
936 10
 
0.7%
816 9
 
0.6%
768 8
 
0.5%
1008 8
 
0.5%
1152 7
 
0.5%
1072 7
 
0.5%
Other values (779) 1354
92.8%
ValueCountFrequency (%)
407 1
 
0.1%
432 1
 
0.1%
442 1
 
0.1%
448 1
 
0.1%
453 1
 
0.1%
483 6
0.4%
492 1
 
0.1%
494 1
 
0.1%
498 1
 
0.1%
502 1
 
0.1%
ValueCountFrequency (%)
5095 1
0.1%
3820 1
0.1%
2726 1
0.1%
2696 1
0.1%
2674 1
0.1%
2552 1
0.1%
2522 1
0.1%
2497 1
0.1%
2492 1
0.1%
2490 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct407
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.96779
Minimum0
Maximum1862
Zeros839
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:54.677560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3676
95-th percentile1116.8
Maximum1862
Range1862
Interquartile range (IQR)676

Descriptive statistics

Standard deviation420.61023
Coefficient of variation (CV)1.2903429
Kurtosis-0.27544098
Mean325.96779
Median Absolute Deviation (MAD)0
Skewness0.91288263
Sum475587
Variance176912.96
MonotonicityNot monotonic
2025-06-03T13:16:54.735872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 839
57.5%
546 15
 
1.0%
504 8
 
0.5%
728 8
 
0.5%
886 7
 
0.5%
720 6
 
0.4%
600 6
 
0.4%
896 5
 
0.3%
601 5
 
0.3%
462 5
 
0.3%
Other values (397) 555
38.0%
ValueCountFrequency (%)
0 839
57.5%
125 1
 
0.1%
144 1
 
0.1%
180 1
 
0.1%
182 1
 
0.1%
185 1
 
0.1%
208 1
 
0.1%
218 1
 
0.1%
228 1
 
0.1%
240 1
 
0.1%
ValueCountFrequency (%)
1862 1
 
0.1%
1836 1
 
0.1%
1788 1
 
0.1%
1778 1
 
0.1%
1721 1
 
0.1%
1629 1
 
0.1%
1619 3
0.2%
1567 1
 
0.1%
1420 1
 
0.1%
1407 1
 
0.1%

LowQualFinSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.543523
Minimum0
Maximum1064
Zeros1445
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:54.786386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1064
Range1064
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.043251
Coefficient of variation (CV)12.429227
Kurtosis308.67691
Mean3.543523
Median Absolute Deviation (MAD)0
Skewness16.167254
Sum5170
Variance1939.8079
MonotonicityNot monotonic
2025-06-03T13:16:54.833618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1445
99.0%
362 1
 
0.1%
1064 1
 
0.1%
431 1
 
0.1%
436 1
 
0.1%
259 1
 
0.1%
312 1
 
0.1%
108 1
 
0.1%
697 1
 
0.1%
512 1
 
0.1%
Other values (5) 5
 
0.3%
ValueCountFrequency (%)
0 1445
99.0%
80 1
 
0.1%
108 1
 
0.1%
114 1
 
0.1%
140 1
 
0.1%
205 1
 
0.1%
259 1
 
0.1%
312 1
 
0.1%
362 1
 
0.1%
431 1
 
0.1%
ValueCountFrequency (%)
1064 1
0.1%
697 1
0.1%
512 1
0.1%
450 1
0.1%
436 1
0.1%
431 1
0.1%
362 1
0.1%
312 1
0.1%
259 1
0.1%
205 1
0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct879
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1486.0459
Minimum407
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:54.885735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum407
5-th percentile864
Q11117.5
median1432
Q31721
95-th percentile2461.3
Maximum5095
Range4688
Interquartile range (IQR)603.5

Descriptive statistics

Standard deviation485.5661
Coefficient of variation (CV)0.3267504
Kurtosis2.9203451
Mean1486.0459
Median Absolute Deviation (MAD)299
Skewness1.1304024
Sum2168141
Variance235774.44
MonotonicityNot monotonic
2025-06-03T13:16:54.942830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 19
 
1.3%
1092 18
 
1.2%
1040 11
 
0.8%
1456 10
 
0.7%
1200 9
 
0.6%
936 9
 
0.6%
960 7
 
0.5%
988 6
 
0.4%
1728 6
 
0.4%
816 6
 
0.4%
Other values (869) 1358
93.1%
ValueCountFrequency (%)
407 1
0.1%
492 1
0.1%
498 1
0.1%
540 1
0.1%
572 1
0.1%
599 1
0.1%
612 1
0.1%
630 1
0.1%
640 1
0.1%
641 1
0.1%
ValueCountFrequency (%)
5095 1
0.1%
3820 1
0.1%
3672 1
0.1%
3500 1
0.1%
3390 1
0.1%
3086 1
0.1%
3078 1
0.1%
3005 1
0.1%
2956 1
0.1%
2944 1
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing2
Missing (%)0.1%
Memory size11.5 KiB
0.0
849 
1.0
584 
2.0
 
23
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4371
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 849
58.2%
1.0 584
40.0%
2.0 23
 
1.6%
3.0 1
 
0.1%
(Missing) 2
 
0.1%

Length

2025-06-03T13:16:54.994855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.037759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 849
58.3%
1.0 584
40.1%
2.0 23
 
1.6%
3.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2306
52.8%
. 1457
33.3%
1 584
 
13.4%
2 23
 
0.5%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2306
52.8%
. 1457
33.3%
1 584
 
13.4%
2 23
 
0.5%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2306
52.8%
. 1457
33.3%
1 584
 
13.4%
2 23
 
0.5%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2306
52.8%
. 1457
33.3%
1 584
 
13.4%
2 23
 
0.5%
3 1
 
< 0.1%

BsmtHalfBath
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing2
Missing (%)0.1%
Memory size11.5 KiB
0.0
1364 
1.0
 
91
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4371
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1364
93.5%
1.0 91
 
6.2%
2.0 2
 
0.1%
(Missing) 2
 
0.1%

Length

2025-06-03T13:16:55.085395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.126959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1364
93.6%
1.0 91
 
6.2%
2.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2821
64.5%
. 1457
33.3%
1 91
 
2.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2821
64.5%
. 1457
33.3%
1 91
 
2.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2821
64.5%
. 1457
33.3%
1 91
 
2.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2821
64.5%
. 1457
33.3%
1 91
 
2.1%
2 2
 
< 0.1%

FullBath
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
762 
1
659 
3
 
31
4
 
4
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

Length

2025-06-03T13:16:55.172116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.218455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 762
52.2%
1 659
45.2%
3 31
 
2.1%
4 4
 
0.3%
0 3
 
0.2%

HalfBath
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
921 
1
525 
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

Length

2025-06-03T13:16:55.269879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.313369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 921
63.1%
1 525
36.0%
2 13
 
0.9%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8540096
Minimum0
Maximum6
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:55.350987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82978836
Coefficient of variation (CV)0.29074477
Kurtosis1.6859653
Mean2.8540096
Median Absolute Deviation (MAD)0
Skewness0.43662328
Sum4164
Variance0.68854873
MonotonicityNot monotonic
2025-06-03T13:16:55.391914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 792
54.3%
2 384
26.3%
4 187
 
12.8%
1 53
 
3.6%
5 27
 
1.9%
6 14
 
1.0%
0 2
 
0.1%
ValueCountFrequency (%)
0 2
 
0.1%
1 53
 
3.6%
2 384
26.3%
3 792
54.3%
4 187
 
12.8%
5 27
 
1.9%
6 14
 
1.0%
ValueCountFrequency (%)
6 14
 
1.0%
5 27
 
1.9%
4 187
 
12.8%
3 792
54.3%
2 384
26.3%
1 53
 
3.6%
0 2
 
0.1%

KitchenAbvGr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1393 
2
 
64
0
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

Length

2025-06-03T13:16:55.438724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.480985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1393
95.5%
2 64
 
4.4%
0 2
 
0.1%

KitchenQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
TA
757 
Gd
565 
Ex
105 
Fa
 
31

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2916
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowGd
3rd rowTA
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 757
51.9%
Gd 565
38.7%
Ex 105
 
7.2%
Fa 31
 
2.1%
(Missing) 1
 
0.1%

Length

2025-06-03T13:16:55.524898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.568693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 757
51.9%
gd 565
38.8%
ex 105
 
7.2%
fa 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
T 757
26.0%
A 757
26.0%
G 565
19.4%
d 565
19.4%
E 105
 
3.6%
x 105
 
3.6%
F 31
 
1.1%
a 31
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 757
26.0%
A 757
26.0%
G 565
19.4%
d 565
19.4%
E 105
 
3.6%
x 105
 
3.6%
F 31
 
1.1%
a 31
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 757
26.0%
A 757
26.0%
G 565
19.4%
d 565
19.4%
E 105
 
3.6%
x 105
 
3.6%
F 31
 
1.1%
a 31
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 757
26.0%
A 757
26.0%
G 565
19.4%
d 565
19.4%
E 105
 
3.6%
x 105
 
3.6%
F 31
 
1.1%
a 31
 
1.1%

TotRmsAbvGrd
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3851953
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:55.611884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum15
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5088946
Coefficient of variation (CV)0.23631142
Kurtosis1.5225956
Mean6.3851953
Median Absolute Deviation (MAD)1
Skewness0.84259745
Sum9316
Variance2.2767628
MonotonicityNot monotonic
2025-06-03T13:16:55.656360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 442
30.3%
7 320
21.9%
5 308
21.1%
8 160
 
11.0%
4 99
 
6.8%
9 68
 
4.7%
10 33
 
2.3%
11 14
 
1.0%
3 8
 
0.5%
12 5
 
0.3%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
3 8
 
0.5%
4 99
 
6.8%
5 308
21.1%
6 442
30.3%
7 320
21.9%
8 160
 
11.0%
9 68
 
4.7%
10 33
 
2.3%
11 14
 
1.0%
12 5
 
0.3%
ValueCountFrequency (%)
15 1
 
0.1%
13 1
 
0.1%
12 5
 
0.3%
11 14
 
1.0%
10 33
 
2.3%
9 68
 
4.7%
8 160
 
11.0%
7 320
21.9%
6 442
30.3%
5 308
21.1%

Functional
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing2
Missing (%)0.1%
Memory size11.5 KiB
Typ
1357 
Min2
 
36
Min1
 
34
Mod
 
20
Maj1
 
5
Other values (2)
 
5

Length

Max length4
Median length3
Mean length3.054221
Min length3

Characters and Unicode

Total characters4450
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1357
93.0%
Min2 36
 
2.5%
Min1 34
 
2.3%
Mod 20
 
1.4%
Maj1 5
 
0.3%
Maj2 4
 
0.3%
Sev 1
 
0.1%
(Missing) 2
 
0.1%

Length

2025-06-03T13:16:55.705238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.752428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
typ 1357
93.1%
min2 36
 
2.5%
min1 34
 
2.3%
mod 20
 
1.4%
maj1 5
 
0.3%
maj2 4
 
0.3%
sev 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1357
30.5%
y 1357
30.5%
p 1357
30.5%
M 99
 
2.2%
i 70
 
1.6%
n 70
 
1.6%
2 40
 
0.9%
1 39
 
0.9%
o 20
 
0.4%
d 20
 
0.4%
Other values (5) 21
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1357
30.5%
y 1357
30.5%
p 1357
30.5%
M 99
 
2.2%
i 70
 
1.6%
n 70
 
1.6%
2 40
 
0.9%
1 39
 
0.9%
o 20
 
0.4%
d 20
 
0.4%
Other values (5) 21
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1357
30.5%
y 1357
30.5%
p 1357
30.5%
M 99
 
2.2%
i 70
 
1.6%
n 70
 
1.6%
2 40
 
0.9%
1 39
 
0.9%
o 20
 
0.4%
d 20
 
0.4%
Other values (5) 21
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1357
30.5%
y 1357
30.5%
p 1357
30.5%
M 99
 
2.2%
i 70
 
1.6%
n 70
 
1.6%
2 40
 
0.9%
1 39
 
0.9%
o 20
 
0.4%
d 20
 
0.4%
Other values (5) 21
 
0.5%

Fireplaces
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
730 
1
618 
2
104 
3
 
6
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

Length

2025-06-03T13:16:55.803729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.847690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 730
50.0%
1 618
42.4%
2 104
 
7.1%
3 6
 
0.4%
4 1
 
0.1%

FireplaceQu
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.7%
Missing730
Missing (%)50.0%
Memory size11.5 KiB
Gd
364 
TA
279 
Fa
41 
Po
 
26
Ex
 
19

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1458
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowGd
3rd rowTA
4th rowGd
5th rowPo

Common Values

ValueCountFrequency (%)
Gd 364
24.9%
TA 279
 
19.1%
Fa 41
 
2.8%
Po 26
 
1.8%
Ex 19
 
1.3%
(Missing) 730
50.0%

Length

2025-06-03T13:16:55.896492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:55.940290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gd 364
49.9%
ta 279
38.3%
fa 41
 
5.6%
po 26
 
3.6%
ex 19
 
2.6%

Most occurring characters

ValueCountFrequency (%)
G 364
25.0%
d 364
25.0%
T 279
19.1%
A 279
19.1%
F 41
 
2.8%
a 41
 
2.8%
P 26
 
1.8%
o 26
 
1.8%
E 19
 
1.3%
x 19
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 364
25.0%
d 364
25.0%
T 279
19.1%
A 279
19.1%
F 41
 
2.8%
a 41
 
2.8%
P 26
 
1.8%
o 26
 
1.8%
E 19
 
1.3%
x 19
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 364
25.0%
d 364
25.0%
T 279
19.1%
A 279
19.1%
F 41
 
2.8%
a 41
 
2.8%
P 26
 
1.8%
o 26
 
1.8%
E 19
 
1.3%
x 19
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 364
25.0%
d 364
25.0%
T 279
19.1%
A 279
19.1%
F 41
 
2.8%
a 41
 
2.8%
P 26
 
1.8%
o 26
 
1.8%
E 19
 
1.3%
x 19
 
1.3%

GarageType
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.4%
Missing76
Missing (%)5.2%
Memory size11.5 KiB
Attchd
853 
Detchd
392 
BuiltIn
98 
Basment
 
17
2Types
 
17

Length

Max length7
Median length6
Mean length6.087491
Min length6

Characters and Unicode

Total characters8419
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowAttchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 853
58.5%
Detchd 392
26.9%
BuiltIn 98
 
6.7%
Basment 17
 
1.2%
2Types 17
 
1.2%
CarPort 6
 
0.4%
(Missing) 76
 
5.2%

Length

2025-06-03T13:16:55.989254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:56.035255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
attchd 853
61.7%
detchd 392
28.3%
builtin 98
 
7.1%
basment 17
 
1.2%
2types 17
 
1.2%
carport 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 2219
26.4%
c 1245
14.8%
h 1245
14.8%
d 1245
14.8%
A 853
 
10.1%
e 426
 
5.1%
D 392
 
4.7%
n 115
 
1.4%
B 115
 
1.4%
u 98
 
1.2%
Other values (14) 466
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2219
26.4%
c 1245
14.8%
h 1245
14.8%
d 1245
14.8%
A 853
 
10.1%
e 426
 
5.1%
D 392
 
4.7%
n 115
 
1.4%
B 115
 
1.4%
u 98
 
1.2%
Other values (14) 466
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2219
26.4%
c 1245
14.8%
h 1245
14.8%
d 1245
14.8%
A 853
 
10.1%
e 426
 
5.1%
D 392
 
4.7%
n 115
 
1.4%
B 115
 
1.4%
u 98
 
1.2%
Other values (14) 466
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2219
26.4%
c 1245
14.8%
h 1245
14.8%
d 1245
14.8%
A 853
 
10.1%
e 426
 
5.1%
D 392
 
4.7%
n 115
 
1.4%
B 115
 
1.4%
u 98
 
1.2%
Other values (14) 466
 
5.5%

GarageYrBlt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)7.0%
Missing78
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean1977.7212
Minimum1895
Maximum2207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:56.089203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1895
5-th percentile1926
Q11959
median1979
Q32002
95-th percentile2007
Maximum2207
Range312
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.431175
Coefficient of variation (CV)0.013364459
Kurtosis3.4979351
Mean1977.7212
Median Absolute Deviation (MAD)21
Skewness-0.15836343
Sum2731233
Variance698.60701
MonotonicityNot monotonic
2025-06-03T13:16:56.148352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 77
 
5.3%
2007 66
 
4.5%
2006 56
 
3.8%
2004 46
 
3.2%
2003 42
 
2.9%
2008 32
 
2.2%
1977 31
 
2.1%
2000 28
 
1.9%
1993 27
 
1.9%
1998 27
 
1.9%
Other values (87) 949
65.0%
(Missing) 78
 
5.3%
ValueCountFrequency (%)
1895 1
 
0.1%
1896 1
 
0.1%
1900 5
 
0.3%
1910 7
 
0.5%
1915 5
 
0.3%
1916 1
 
0.1%
1917 2
 
0.1%
1918 1
 
0.1%
1919 1
 
0.1%
1920 19
1.3%
ValueCountFrequency (%)
2207 1
 
0.1%
2010 2
 
0.1%
2009 8
 
0.5%
2008 32
2.2%
2007 66
4.5%
2006 56
3.8%
2005 77
5.3%
2004 46
3.2%
2003 42
2.9%
2002 27
 
1.9%

GarageFinish
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.2%
Missing78
Missing (%)5.3%
Memory size11.5 KiB
Unf
625 
RFn
389 
Fin
367 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4143
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowFin
4th rowFin
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 625
42.8%
RFn 389
26.7%
Fin 367
25.2%
(Missing) 78
 
5.3%

Length

2025-06-03T13:16:56.284864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:56.326357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unf 625
45.3%
rfn 389
28.2%
fin 367
26.6%

Most occurring characters

ValueCountFrequency (%)
n 1381
33.3%
F 756
18.2%
U 625
15.1%
f 625
15.1%
R 389
 
9.4%
i 367
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1381
33.3%
F 756
18.2%
U 625
15.1%
f 625
15.1%
R 389
 
9.4%
i 367
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1381
33.3%
F 756
18.2%
U 625
15.1%
f 625
15.1%
R 389
 
9.4%
i 367
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1381
33.3%
F 756
18.2%
U 625
15.1%
f 625
15.1%
R 389
 
9.4%
i 367
 
8.9%

GarageCars
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.766118
Minimum0
Maximum5
Zeros76
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:56.365415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77594507
Coefficient of variation (CV)0.43935065
Kurtosis0.24961012
Mean1.766118
Median Absolute Deviation (MAD)0
Skewness-0.10714152
Sum2575
Variance0.60209075
MonotonicityNot monotonic
2025-06-03T13:16:56.407141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 770
52.8%
1 407
27.9%
3 193
 
13.2%
0 76
 
5.2%
4 11
 
0.8%
5 1
 
0.1%
(Missing) 1
 
0.1%
ValueCountFrequency (%)
0 76
 
5.2%
1 407
27.9%
2 770
52.8%
3 193
 
13.2%
4 11
 
0.8%
5 1
 
0.1%
ValueCountFrequency (%)
5 1
 
0.1%
4 11
 
0.8%
3 193
 
13.2%
2 770
52.8%
1 407
27.9%
0 76
 
5.2%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct459
Distinct (%)31.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean472.76886
Minimum0
Maximum1488
Zeros76
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:56.456539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1318
median480
Q3576
95-th percentile864
Maximum1488
Range1488
Interquartile range (IQR)258

Descriptive statistics

Standard deviation217.04861
Coefficient of variation (CV)0.4591009
Kurtosis0.96685212
Mean472.76886
Median Absolute Deviation (MAD)129.5
Skewness0.30023887
Sum689297
Variance47110.1
MonotonicityNot monotonic
2025-06-03T13:16:56.516073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
5.2%
576 50
 
3.4%
440 47
 
3.2%
484 34
 
2.3%
400 33
 
2.3%
528 32
 
2.2%
240 31
 
2.1%
480 30
 
2.1%
308 28
 
1.9%
264 27
 
1.9%
Other values (449) 1070
73.3%
ValueCountFrequency (%)
0 76
5.2%
100 1
 
0.1%
160 1
 
0.1%
162 2
 
0.1%
164 1
 
0.1%
180 7
 
0.5%
184 1
 
0.1%
185 1
 
0.1%
195 3
 
0.2%
200 7
 
0.5%
ValueCountFrequency (%)
1488 1
0.1%
1348 1
0.1%
1314 1
0.1%
1231 1
0.1%
1200 1
0.1%
1184 1
0.1%
1174 1
0.1%
1154 1
0.1%
1150 1
0.1%
1138 1
0.1%

GarageQual
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.3%
Missing78
Missing (%)5.3%
Memory size11.5 KiB
TA
1293 
Fa
 
76
Gd
 
10
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2762
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1293
88.6%
Fa 76
 
5.2%
Gd 10
 
0.7%
Po 2
 
0.1%
(Missing) 78
 
5.3%

Length

2025-06-03T13:16:56.571705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:56.612726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1293
93.6%
fa 76
 
5.5%
gd 10
 
0.7%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1293
46.8%
A 1293
46.8%
F 76
 
2.8%
a 76
 
2.8%
G 10
 
0.4%
d 10
 
0.4%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1293
46.8%
A 1293
46.8%
F 76
 
2.8%
a 76
 
2.8%
G 10
 
0.4%
d 10
 
0.4%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1293
46.8%
A 1293
46.8%
F 76
 
2.8%
a 76
 
2.8%
G 10
 
0.4%
d 10
 
0.4%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1293
46.8%
A 1293
46.8%
F 76
 
2.8%
a 76
 
2.8%
G 10
 
0.4%
d 10
 
0.4%
P 2
 
0.1%
o 2
 
0.1%

GarageCond
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)0.4%
Missing78
Missing (%)5.3%
Memory size11.5 KiB
TA
1328 
Fa
 
39
Po
 
7
Gd
 
6
Ex
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2762
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1328
91.0%
Fa 39
 
2.7%
Po 7
 
0.5%
Gd 6
 
0.4%
Ex 1
 
0.1%
(Missing) 78
 
5.3%

Length

2025-06-03T13:16:56.657993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:56.700213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1328
96.2%
fa 39
 
2.8%
po 7
 
0.5%
gd 6
 
0.4%
ex 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1328
48.1%
A 1328
48.1%
F 39
 
1.4%
a 39
 
1.4%
P 7
 
0.3%
o 7
 
0.3%
G 6
 
0.2%
d 6
 
0.2%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1328
48.1%
A 1328
48.1%
F 39
 
1.4%
a 39
 
1.4%
P 7
 
0.3%
o 7
 
0.3%
G 6
 
0.2%
d 6
 
0.2%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1328
48.1%
A 1328
48.1%
F 39
 
1.4%
a 39
 
1.4%
P 7
 
0.3%
o 7
 
0.3%
G 6
 
0.2%
d 6
 
0.2%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1328
48.1%
A 1328
48.1%
F 39
 
1.4%
a 39
 
1.4%
P 7
 
0.3%
o 7
 
0.3%
G 6
 
0.2%
d 6
 
0.2%
E 1
 
< 0.1%
x 1
 
< 0.1%

PavedDrive
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Y
1301 
N
 
126
P
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1301
89.2%
N 126
 
8.6%
P 32
 
2.2%

Length

2025-06-03T13:16:56.747039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:56.788296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
y 1301
89.2%
n 126
 
8.6%
p 32
 
2.2%

Most occurring characters

ValueCountFrequency (%)
Y 1301
89.2%
N 126
 
8.6%
P 32
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1301
89.2%
N 126
 
8.6%
P 32
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1301
89.2%
N 126
 
8.6%
P 32
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1301
89.2%
N 126
 
8.6%
P 32
 
2.2%

WoodDeckSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct263
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.174777
Minimum0
Maximum1424
Zeros762
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:56.835843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile319
Maximum1424
Range1424
Interquartile range (IQR)168

Descriptive statistics

Standard deviation127.74488
Coefficient of variation (CV)1.3710243
Kurtosis10.249278
Mean93.174777
Median Absolute Deviation (MAD)0
Skewness2.13076
Sum135942
Variance16318.755
MonotonicityNot monotonic
2025-06-03T13:16:56.891817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762
52.2%
100 38
 
2.6%
192 32
 
2.2%
168 28
 
1.9%
144 28
 
1.9%
120 22
 
1.5%
140 14
 
1.0%
200 11
 
0.8%
240 10
 
0.7%
160 9
 
0.6%
Other values (253) 505
34.6%
ValueCountFrequency (%)
0 762
52.2%
4 1
 
0.1%
14 1
 
0.1%
16 1
 
0.1%
20 1
 
0.1%
22 1
 
0.1%
23 1
 
0.1%
24 3
 
0.2%
25 2
 
0.1%
27 1
 
0.1%
ValueCountFrequency (%)
1424 1
0.1%
870 1
0.1%
690 1
0.1%
684 1
0.1%
657 1
0.1%
646 1
0.1%
641 1
0.1%
631 1
0.1%
546 1
0.1%
530 1
0.1%

OpenPorchSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct203
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.313914
Minimum0
Maximum742
Zeros642
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:56.949359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q372
95-th percentile189
Maximum742
Range742
Interquartile range (IQR)72

Descriptive statistics

Standard deviation68.883364
Coefficient of variation (CV)1.4257459
Kurtosis13.010836
Mean48.313914
Median Absolute Deviation (MAD)28
Skewness2.6877789
Sum70490
Variance4744.9179
MonotonicityNot monotonic
2025-06-03T13:16:57.008176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 642
44.0%
48 29
 
2.0%
32 27
 
1.9%
40 25
 
1.7%
36 23
 
1.6%
28 21
 
1.4%
24 20
 
1.4%
50 16
 
1.1%
64 15
 
1.0%
30 15
 
1.0%
Other values (193) 626
42.9%
ValueCountFrequency (%)
0 642
44.0%
6 1
 
0.1%
10 1
 
0.1%
11 2
 
0.1%
12 2
 
0.1%
15 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
20 12
 
0.8%
21 8
 
0.5%
ValueCountFrequency (%)
742 1
0.1%
570 1
0.1%
484 1
0.1%
444 1
0.1%
382 1
0.1%
372 1
0.1%
368 1
0.1%
365 1
0.1%
341 1
0.1%
324 1
0.1%

EnclosedPorch
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct131
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.243317
Minimum0
Maximum1012
Zeros1208
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:57.068875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile169.1
Maximum1012
Range1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation67.227765
Coefficient of variation (CV)2.7730432
Kurtosis40.129017
Mean24.243317
Median Absolute Deviation (MAD)0
Skewness4.6691723
Sum35371
Variance4519.5724
MonotonicityNot monotonic
2025-06-03T13:16:57.130200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1208
82.8%
96 7
 
0.5%
168 7
 
0.5%
112 7
 
0.5%
144 6
 
0.4%
84 6
 
0.4%
192 5
 
0.3%
180 5
 
0.3%
60 5
 
0.3%
160 5
 
0.3%
Other values (121) 198
 
13.6%
ValueCountFrequency (%)
0 1208
82.8%
16 1
 
0.1%
18 1
 
0.1%
20 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 1
 
0.1%
26 1
 
0.1%
28 1
 
0.1%
30 2
 
0.1%
ValueCountFrequency (%)
1012 1
0.1%
584 1
0.1%
432 1
0.1%
429 1
0.1%
368 1
0.1%
364 1
0.1%
334 1
0.1%
324 1
0.1%
296 1
0.1%
290 1
0.1%

3SsnPorch
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7943797
Minimum0
Maximum360
Zeros1446
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:57.180016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.207842
Coefficient of variation (CV)11.261742
Kurtosis170.20011
Mean1.7943797
Median Absolute Deviation (MAD)0
Skewness12.524216
Sum2618
Variance408.35687
MonotonicityNot monotonic
2025-06-03T13:16:57.228192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 1446
99.1%
153 2
 
0.1%
224 1
 
0.1%
255 1
 
0.1%
225 1
 
0.1%
360 1
 
0.1%
150 1
 
0.1%
174 1
 
0.1%
120 1
 
0.1%
219 1
 
0.1%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
0 1446
99.1%
86 1
 
0.1%
120 1
 
0.1%
150 1
 
0.1%
153 2
 
0.1%
174 1
 
0.1%
176 1
 
0.1%
219 1
 
0.1%
224 1
 
0.1%
225 1
 
0.1%
ValueCountFrequency (%)
360 1
0.1%
323 1
0.1%
255 1
0.1%
225 1
0.1%
224 1
0.1%
219 1
0.1%
176 1
0.1%
174 1
0.1%
153 2
0.1%
150 1
0.1%

ScreenPorch
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.064428
Minimum0
Maximum576
Zeros1319
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:57.286159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile162.2
Maximum576
Range576
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.609763
Coefficient of variation (CV)3.3174135
Kurtosis17.239542
Mean17.064428
Median Absolute Deviation (MAD)0
Skewness3.7882444
Sum24897
Variance3204.6653
MonotonicityNot monotonic
2025-06-03T13:16:57.349940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1319
90.4%
144 10
 
0.7%
168 7
 
0.5%
216 6
 
0.4%
192 5
 
0.3%
200 5
 
0.3%
120 4
 
0.3%
156 3
 
0.2%
225 3
 
0.2%
100 3
 
0.2%
Other values (65) 94
 
6.4%
ValueCountFrequency (%)
0 1319
90.4%
64 1
 
0.1%
84 1
 
0.1%
88 1
 
0.1%
92 2
 
0.1%
94 1
 
0.1%
95 1
 
0.1%
100 3
 
0.2%
104 1
 
0.1%
108 2
 
0.1%
ValueCountFrequency (%)
576 1
0.1%
490 1
0.1%
348 1
0.1%
342 1
0.1%
322 1
0.1%
288 2
0.1%
280 1
0.1%
270 1
0.1%
266 1
0.1%
264 1
0.1%

PoolArea
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7443454
Minimum0
Maximum800
Zeros1453
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:57.397683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum800
Range800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.491646
Coefficient of variation (CV)17.48028
Kurtosis445.6611
Mean1.7443454
Median Absolute Deviation (MAD)0
Skewness20.196888
Sum2545
Variance929.74049
MonotonicityNot monotonic
2025-06-03T13:16:57.444409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1453
99.6%
144 1
 
0.1%
368 1
 
0.1%
444 1
 
0.1%
228 1
 
0.1%
561 1
 
0.1%
800 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.6%
144 1
 
0.1%
228 1
 
0.1%
368 1
 
0.1%
444 1
 
0.1%
561 1
 
0.1%
800 1
 
0.1%
ValueCountFrequency (%)
800 1
 
0.1%
561 1
 
0.1%
444 1
 
0.1%
368 1
 
0.1%
228 1
 
0.1%
144 1
 
0.1%
0 1453
99.6%

PoolQC
Text

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)66.7%
Missing1456
Missing (%)99.8%
Memory size11.5 KiB
2025-06-03T13:16:57.496923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowEx
2nd rowEx
3rd rowGd
ValueCountFrequency (%)
ex 2
66.7%
gd 1
33.3%
2025-06-03T13:16:57.712094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2
33.3%
x 2
33.3%
G 1
16.7%
d 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2
33.3%
x 2
33.3%
G 1
16.7%
d 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2
33.3%
x 2
33.3%
G 1
16.7%
d 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2
33.3%
x 2
33.3%
G 1
16.7%
d 1
16.7%

Fence
Categorical

MISSING 

Distinct4
Distinct (%)1.4%
Missing1169
Missing (%)80.1%
Memory size11.5 KiB
MnPrv
172 
GdPrv
59 
GdWo
58 
MnWw
 
1

Length

Max length5
Median length5
Mean length4.7965517
Min length4

Characters and Unicode

Total characters1391
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowMnPrv
2nd rowMnPrv
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv

Common Values

ValueCountFrequency (%)
MnPrv 172
 
11.8%
GdPrv 59
 
4.0%
GdWo 58
 
4.0%
MnWw 1
 
0.1%
(Missing) 1169
80.1%

Length

2025-06-03T13:16:57.766688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:57.808898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
mnprv 172
59.3%
gdprv 59
 
20.3%
gdwo 58
 
20.0%
mnww 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
P 231
16.6%
r 231
16.6%
v 231
16.6%
M 173
12.4%
n 173
12.4%
G 117
8.4%
d 117
8.4%
W 59
 
4.2%
o 58
 
4.2%
w 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 231
16.6%
r 231
16.6%
v 231
16.6%
M 173
12.4%
n 173
12.4%
G 117
8.4%
d 117
8.4%
W 59
 
4.2%
o 58
 
4.2%
w 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 231
16.6%
r 231
16.6%
v 231
16.6%
M 173
12.4%
n 173
12.4%
G 117
8.4%
d 117
8.4%
W 59
 
4.2%
o 58
 
4.2%
w 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 231
16.6%
r 231
16.6%
v 231
16.6%
M 173
12.4%
n 173
12.4%
G 117
8.4%
d 117
8.4%
W 59
 
4.2%
o 58
 
4.2%
w 1
 
0.1%

MiscFeature
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)5.9%
Missing1408
Missing (%)96.5%
Memory size11.5 KiB
Shed
46 
Gar2
 
3
Othr
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters204
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGar2
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed

Common Values

ValueCountFrequency (%)
Shed 46
 
3.2%
Gar2 3
 
0.2%
Othr 2
 
0.1%
(Missing) 1408
96.5%

Length

2025-06-03T13:16:57.860311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:57.900241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shed 46
90.2%
gar2 3
 
5.9%
othr 2
 
3.9%

Most occurring characters

ValueCountFrequency (%)
h 48
23.5%
S 46
22.5%
e 46
22.5%
d 46
22.5%
r 5
 
2.5%
G 3
 
1.5%
a 3
 
1.5%
2 3
 
1.5%
O 2
 
1.0%
t 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 48
23.5%
S 46
22.5%
e 46
22.5%
d 46
22.5%
r 5
 
2.5%
G 3
 
1.5%
a 3
 
1.5%
2 3
 
1.5%
O 2
 
1.0%
t 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 48
23.5%
S 46
22.5%
e 46
22.5%
d 46
22.5%
r 5
 
2.5%
G 3
 
1.5%
a 3
 
1.5%
2 3
 
1.5%
O 2
 
1.0%
t 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 48
23.5%
S 46
22.5%
e 46
22.5%
d 46
22.5%
r 5
 
2.5%
G 3
 
1.5%
a 3
 
1.5%
2 3
 
1.5%
O 2
 
1.0%
t 2
 
1.0%

MiscVal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct26
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.167923
Minimum0
Maximum17000
Zeros1408
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:57.942293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17000
Range17000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation630.80698
Coefficient of variation (CV)10.844585
Kurtosis471.51739
Mean58.167923
Median Absolute Deviation (MAD)0
Skewness20.075188
Sum84867
Variance397917.44
MonotonicityNot monotonic
2025-06-03T13:16:57.993899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 1408
96.5%
400 7
 
0.5%
500 5
 
0.3%
450 5
 
0.3%
600 4
 
0.3%
650 3
 
0.2%
2000 3
 
0.2%
1500 3
 
0.2%
3000 2
 
0.1%
4500 2
 
0.1%
Other values (16) 17
 
1.2%
ValueCountFrequency (%)
0 1408
96.5%
80 1
 
0.1%
300 1
 
0.1%
400 7
 
0.5%
420 1
 
0.1%
450 5
 
0.3%
455 1
 
0.1%
460 1
 
0.1%
490 1
 
0.1%
500 5
 
0.3%
ValueCountFrequency (%)
17000 1
 
0.1%
12500 1
 
0.1%
6500 1
 
0.1%
4500 2
0.1%
3000 2
0.1%
2500 1
 
0.1%
2000 3
0.2%
1512 1
 
0.1%
1500 3
0.2%
1200 1
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1041809
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-06-03T13:16:58.041626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7224319
Coefficient of variation (CV)0.44599463
Kurtosis-0.5077961
Mean6.1041809
Median Absolute Deviation (MAD)2
Skewness0.18302231
Sum8906
Variance7.4116355
MonotonicityNot monotonic
2025-06-03T13:16:58.085512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 250
17.1%
7 212
14.5%
5 190
13.0%
4 138
9.5%
3 126
8.6%
8 111
7.6%
9 95
 
6.5%
10 84
 
5.8%
2 81
 
5.6%
1 64
 
4.4%
Other values (2) 108
7.4%
ValueCountFrequency (%)
1 64
 
4.4%
2 81
 
5.6%
3 126
8.6%
4 138
9.5%
5 190
13.0%
6 250
17.1%
7 212
14.5%
8 111
7.6%
9 95
 
6.5%
10 84
 
5.8%
ValueCountFrequency (%)
12 45
 
3.1%
11 63
 
4.3%
10 84
 
5.8%
9 95
 
6.5%
8 111
7.6%
7 212
14.5%
6 250
17.1%
5 190
13.0%
4 138
9.5%
3 126
8.6%

YrSold
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2007
363 
2008
318 
2009
309 
2006
305 
2010
164 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5836
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2010
3rd row2010
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2007 363
24.9%
2008 318
21.8%
2009 309
21.2%
2006 305
20.9%
2010 164
11.2%

Length

2025-06-03T13:16:58.133665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:58.194797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2007 363
24.9%
2008 318
21.8%
2009 309
21.2%
2006 305
20.9%
2010 164
11.2%

Most occurring characters

ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
7 363
 
6.2%
8 318
 
5.4%
9 309
 
5.3%
6 305
 
5.2%
1 164
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
7 363
 
6.2%
8 318
 
5.4%
9 309
 
5.3%
6 305
 
5.2%
1 164
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
7 363
 
6.2%
8 318
 
5.4%
9 309
 
5.3%
6 305
 
5.2%
1 164
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
7 363
 
6.2%
8 318
 
5.4%
9 309
 
5.3%
6 305
 
5.2%
1 164
 
2.8%

SaleType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
WD
1258 
New
 
117
COD
 
44
ConLD
 
17
CWD
 
8
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.170096
Min length2

Characters and Unicode

Total characters3164
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1258
86.2%
New 117
 
8.0%
COD 44
 
3.0%
ConLD 17
 
1.2%
CWD 8
 
0.5%
Oth 4
 
0.3%
ConLI 4
 
0.3%
Con 3
 
0.2%
ConLw 3
 
0.2%
(Missing) 1
 
0.1%

Length

2025-06-03T13:16:58.266673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:58.323952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
wd 1258
86.3%
new 117
 
8.0%
cod 44
 
3.0%
conld 17
 
1.2%
cwd 8
 
0.5%
oth 4
 
0.3%
conli 4
 
0.3%
con 3
 
0.2%
conlw 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 1327
41.9%
W 1266
40.0%
w 120
 
3.8%
N 117
 
3.7%
e 117
 
3.7%
C 79
 
2.5%
O 48
 
1.5%
o 27
 
0.9%
n 27
 
0.9%
L 24
 
0.8%
Other values (3) 12
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1327
41.9%
W 1266
40.0%
w 120
 
3.8%
N 117
 
3.7%
e 117
 
3.7%
C 79
 
2.5%
O 48
 
1.5%
o 27
 
0.9%
n 27
 
0.9%
L 24
 
0.8%
Other values (3) 12
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1327
41.9%
W 1266
40.0%
w 120
 
3.8%
N 117
 
3.7%
e 117
 
3.7%
C 79
 
2.5%
O 48
 
1.5%
o 27
 
0.9%
n 27
 
0.9%
L 24
 
0.8%
Other values (3) 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1327
41.9%
W 1266
40.0%
w 120
 
3.8%
N 117
 
3.7%
e 117
 
3.7%
C 79
 
2.5%
O 48
 
1.5%
o 27
 
0.9%
n 27
 
0.9%
L 24
 
0.8%
Other values (3) 12
 
0.4%

SaleCondition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Normal
1204 
Partial
 
120
Abnorml
 
89
Family
 
26
Alloca
 
12

Length

Max length7
Median length6
Mean length6.148732
Min length6

Characters and Unicode

Total characters8971
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1204
82.5%
Partial 120
 
8.2%
Abnorml 89
 
6.1%
Family 26
 
1.8%
Alloca 12
 
0.8%
AdjLand 8
 
0.5%

Length

2025-06-03T13:16:58.391555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T13:16:58.438881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 1204
82.5%
partial 120
 
8.2%
abnorml 89
 
6.1%
family 26
 
1.8%
alloca 12
 
0.8%
adjland 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 1490
16.6%
l 1463
16.3%
r 1413
15.8%
m 1319
14.7%
o 1305
14.5%
N 1204
13.4%
i 146
 
1.6%
P 120
 
1.3%
t 120
 
1.3%
A 109
 
1.2%
Other values (8) 282
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1490
16.6%
l 1463
16.3%
r 1413
15.8%
m 1319
14.7%
o 1305
14.5%
N 1204
13.4%
i 146
 
1.6%
P 120
 
1.3%
t 120
 
1.3%
A 109
 
1.2%
Other values (8) 282
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1490
16.6%
l 1463
16.3%
r 1413
15.8%
m 1319
14.7%
o 1305
14.5%
N 1204
13.4%
i 146
 
1.6%
P 120
 
1.3%
t 120
 
1.3%
A 109
 
1.2%
Other values (8) 282
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1490
16.6%
l 1463
16.3%
r 1413
15.8%
m 1319
14.7%
o 1305
14.5%
N 1204
13.4%
i 146
 
1.6%
P 120
 
1.3%
t 120
 
1.3%
A 109
 
1.2%
Other values (8) 282
 
3.1%

Correlations

2025-06-03T13:16:58.604663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleCondition
Id1.0000.2530.4040.1900.2060.0000.5070.1700.1650.1170.1180.6730.1750.0000.2600.1870.3590.1750.4500.3840.1380.0910.2260.2830.1870.2540.2670.1230.2730.3710.0570.1020.1980.1030.0690.0660.1130.1470.0000.2380.1920.1780.1300.2280.0000.1560.0770.0000.3440.2420.1000.1160.2540.2300.0300.2270.0000.2400.3890.3480.2380.2550.1690.0720.2420.1030.0000.1070.0000.0610.0031.0000.0000.0000.0200.2720.9970.2050.232
MSSubClass0.2531.0000.4370.5810.4430.0000.8740.2670.1930.1610.1870.7500.2110.0100.9320.8480.3810.3470.5400.4940.2350.0620.4320.4940.2300.1720.4180.1850.4660.4110.1820.3340.3600.2430.1330.0520.3390.3900.1210.3080.2650.2640.3850.6910.3860.5120.3890.3110.4010.7980.5230.8940.3670.4540.1420.2930.3360.5310.4540.5770.4390.3380.3600.1310.3530.1820.2210.1200.0520.1120.0000.0000.1650.0000.0000.0280.0960.2270.295
MSZoning0.4040.4371.0000.3890.5280.2780.7260.1920.0440.2520.0000.9150.1570.0000.4930.2720.3560.3040.6500.4690.1510.0320.3720.4960.0000.1200.2720.2590.3740.2730.1530.1240.2190.1730.0620.0000.1960.2260.0000.3640.2320.2060.2250.4700.0000.1650.1130.0000.3510.1430.1620.0700.2110.1580.0450.2820.0700.3200.5550.2930.2790.4320.2540.3350.3340.1090.2170.1490.2060.0000.000NaN0.1290.0000.0000.0000.1670.1530.162
LotFrontage0.1900.5810.3891.0000.7550.1900.5230.2140.1100.3240.5800.7030.1750.0000.6390.2260.3340.1960.3830.3330.2860.3860.3040.3860.2210.3710.2340.0000.2770.3450.0360.2340.2070.2670.0000.1630.1640.3500.2170.0970.1410.0800.3980.2820.0000.5000.1630.0900.3020.2850.3180.1030.2730.4220.1650.2830.2220.4690.2660.3810.4260.3540.1370.0000.3590.2880.3500.0480.0000.0890.4950.0000.0000.0000.0760.0000.0420.0000.151
LotArea0.2060.4430.5280.7551.0000.1380.1990.3150.2850.3200.4530.5760.1410.2050.6160.1780.4780.2870.3450.2740.2560.2180.4340.2760.0770.5900.2070.1190.1640.2270.0000.2250.0740.5320.0000.0000.1870.5890.1900.0830.1150.0450.6650.3540.0000.6310.1100.0000.2900.1300.2930.0000.2500.6740.3480.4040.1070.3500.2350.2330.3020.4720.2200.0780.2590.2910.5360.3820.0000.3530.5570.0000.0000.0000.5240.1780.0860.0000.183
Street0.0000.0000.2780.1900.1381.000NaN0.0000.1370.0000.0000.2760.1890.0000.0000.0000.1970.0270.1610.1230.0000.0000.0670.0000.1880.0000.2510.0920.2400.1680.1460.0000.0660.0000.0000.0000.0000.0240.0000.0870.0590.0000.0000.0470.0000.1010.0000.0000.0390.0000.0770.0000.1040.0000.0540.0000.0000.0480.0710.0140.0000.0000.0000.0260.1100.0000.0000.0910.0000.0000.000NaN0.000NaN0.0000.0000.0000.0600.313
Alley0.5070.8740.7260.5230.199NaN1.0000.2760.0260.0000.0000.9930.094NaN0.5760.4710.7470.7200.9470.6780.0000.0000.4380.5250.0000.5310.8540.2640.5110.9300.1960.0000.7820.2080.0000.0000.4610.4150.0000.3130.3530.4170.1960.2430.0000.2200.4310.0000.2550.6430.3640.0000.6610.0920.0000.0000.0000.3650.8730.2970.3150.4190.3580.2030.2240.0000.3940.2460.0000.000NaNNaN0.0000.0000.0000.2490.1120.2860.255
LotShape0.1700.2670.1920.2140.3150.0000.2761.0000.2710.2600.0370.4860.1500.0910.1170.1830.2980.1670.3600.3050.0000.0000.2140.2160.0470.1720.3640.0560.2340.4580.1050.3280.1730.1530.0000.0000.0620.1650.0000.1220.1950.2350.1590.2710.0000.2110.1120.0210.1800.1280.1560.0580.3270.1500.0000.1480.0000.2820.2180.2260.2630.2530.1430.0410.1420.1210.0510.0430.0000.0000.0000.0000.2100.6370.0950.0670.0000.1030.117
LandContour0.1650.1930.0440.1100.2850.1370.0260.2711.0000.0000.4100.6110.1000.1150.0930.1230.2830.1360.2310.1880.1770.3590.2270.1990.1110.1750.3540.0990.1430.3750.1290.4600.1460.2030.1100.1240.0000.1740.2050.1140.1900.1570.1620.0720.1560.1310.1340.0990.1080.0000.1410.0000.3330.2010.1890.1880.0970.0910.1570.1360.1150.1010.1350.0640.0410.2280.0730.0000.2420.1600.1980.0000.0000.0000.0900.0000.0000.0460.124
LotConfig0.1170.1610.2520.3240.3200.0000.0000.2600.0001.0000.0000.3680.1950.0940.2440.0750.0930.0000.2710.2250.0000.0000.0660.3610.0700.0510.0220.0770.0000.1250.0260.1010.1000.0750.0480.0840.1180.0660.0000.0420.0520.0000.0620.0000.0000.0370.0340.0720.2320.0860.1700.0490.0000.1350.0000.1340.1210.0820.1690.1020.0920.1550.0000.0000.0530.1040.0000.0000.0000.0270.0001.0000.0180.0000.0000.0690.0000.0000.000
LandSlope0.1180.1870.0000.5800.4530.0000.0000.0370.4100.0001.0000.3750.0000.0000.0760.0610.1210.0000.1100.0450.5050.4240.0880.0890.2950.3650.0450.0870.0000.0520.0270.1970.1730.1530.2310.2110.0880.0660.1490.0550.0190.0000.0810.0000.0000.0000.0990.1690.2970.0000.4090.0000.0460.0410.0510.1300.0580.1700.0000.0000.0000.0000.0000.0000.0000.2600.0000.0000.0490.1330.0940.0000.0000.3380.0000.0250.0510.0000.034
Neighborhood0.6730.7500.9150.7030.5760.2760.9930.4860.6110.3680.3751.0000.4570.2750.7560.5890.7200.5240.8330.7830.4390.3750.7220.7990.4680.5200.7470.3600.7220.7980.3000.4750.6060.4520.3490.3400.4290.5260.0000.6060.4220.3380.5300.6130.0000.5250.3420.3120.6360.5460.4270.2080.6960.4290.1380.5600.5920.6270.8020.7060.6680.6210.4200.2970.6190.3650.2980.2680.2750.1510.1911.0000.3340.0000.0000.1550.1770.3440.410
Condition10.1750.2110.1570.1750.1410.1890.0940.1500.1000.1950.0000.4571.0000.5590.1250.0890.2330.0890.1970.1590.0000.0640.1980.1110.4440.0590.2240.0980.1420.2230.1470.1170.1370.0940.0890.0000.0770.1410.0000.0470.1660.1560.1080.0690.0000.1620.0000.0000.1580.2240.1000.0000.1680.0870.0000.1420.1380.1590.1770.2920.1380.1310.0000.0000.2900.0570.0000.0780.0000.2470.024NaN0.0000.0000.0000.0600.0580.1360.133
Condition20.0000.0100.0000.0000.2050.000NaN0.0910.1150.0940.0000.2750.5591.0000.0000.0000.5270.0000.0000.0000.0440.0000.0000.0000.0000.0000.2040.0000.0000.1290.0000.1160.0000.2990.0000.0000.1660.2500.0000.0000.0000.0000.1910.0000.0000.0000.0670.0000.0000.0000.0000.0000.1430.0390.0000.0450.0000.0220.0880.0780.0730.0000.0000.0000.0000.0000.0220.0000.0000.0000.000NaN0.0000.0000.0000.0660.0000.0000.075
BldgType0.2600.9320.4930.6390.6160.0000.5760.1170.0930.2440.0760.7560.1250.0001.0000.2410.3340.1620.4890.4720.0680.0400.3460.5260.1170.1690.2240.2740.2370.1890.0920.0880.1810.0890.0330.0000.3580.1950.1150.2960.2040.1700.2020.4010.0000.1560.2720.0780.4150.3230.4590.6620.1960.4890.1060.2900.0520.1940.3670.2090.2960.3900.0310.1040.1570.0720.1030.1110.0590.0000.000NaN0.0000.0000.0000.0000.0000.1120.211
HouseStyle0.1870.8480.2720.2260.1780.0000.4710.1830.1230.0750.0610.5890.0890.0000.2411.0000.3100.2430.5090.4170.1840.0000.3640.3850.2260.1650.2840.0930.3450.3330.1230.3400.2600.3450.1250.1070.3380.4980.0000.1900.1370.2280.4920.7050.2420.3830.2570.2030.2800.5620.5250.1510.2360.4160.1790.1600.3050.4040.3690.3350.2390.2180.2140.1170.2440.2400.0990.1820.0000.0000.0000.0000.0600.0000.0000.0450.0570.1250.123
OverallQual0.3590.3810.3560.3340.4780.1970.7470.2980.2830.0930.1210.7200.2330.5270.3340.3101.0000.4520.6710.6110.3030.0070.6360.5430.2550.5900.7960.5890.4980.6910.2050.3790.4630.4610.1040.0000.3100.5370.4660.7020.5180.3270.4970.4750.1160.5160.2610.0000.5880.3520.2430.1420.7500.5030.2790.4710.3010.3860.6660.5400.5890.6760.3280.4910.4850.2730.2510.0820.0000.1200.2591.0000.0600.0000.2110.1390.0840.3630.321
OverallCond0.1750.3470.3040.1960.2870.0270.7200.1670.1360.0000.0000.5240.0890.0000.1620.2430.4521.0000.5150.3660.2360.0510.4180.4180.2570.1400.4570.5260.4720.4890.2330.1840.3570.1420.0830.1120.1700.2320.1800.2960.2970.2270.2070.2170.1310.3280.0860.1260.3790.3590.0340.1290.3510.1530.4810.2020.1620.3470.4870.5610.3940.3110.2070.3330.5460.1410.1640.1450.1770.0000.0000.0000.0000.0000.0000.0570.0610.2710.272
YearBuilt0.4500.5400.6500.3830.3450.1610.9470.3600.2310.2710.1100.8330.1970.0000.4890.5090.6710.5151.0000.8950.2490.1250.6550.6830.3740.3040.6530.3900.7330.7300.2870.3100.5800.3240.2710.1670.4390.3490.1670.6620.5120.3970.3080.5390.1860.3290.2340.2050.6250.3780.1590.1570.5890.3380.2080.3960.5830.5110.9390.6140.5170.5530.4190.3710.5620.2580.2010.3090.0000.0190.0001.0000.2700.0000.0000.0000.0000.3540.372
YearRemodAdd0.3840.4940.4690.3330.2740.1230.6780.3050.1880.2250.0450.7830.1590.0000.4720.4170.6110.3660.8951.0000.2220.1600.6160.6480.4690.2930.6140.2770.5680.6050.1900.2850.4850.2830.2610.1640.3790.2920.0380.6490.4950.3800.2850.4450.0600.2990.2020.2310.5510.3430.1810.2250.6110.2950.1370.3250.6770.3950.8220.5000.4260.4810.2980.2700.4120.2620.1670.2340.0830.1430.1211.0000.1900.4980.0000.0680.0880.3900.427
RoofStyle0.1380.2350.1510.2860.2560.0000.0000.0000.1770.0000.5050.4390.0000.0440.0680.1840.3030.2360.2490.2221.0000.7350.2180.3440.0000.3280.2720.0000.2660.3380.1570.2020.2100.2060.2070.1490.0830.2520.2490.0540.2050.0000.2630.3000.1250.2260.1910.4140.2440.2430.3050.4010.2810.0950.0340.1390.1180.3360.0850.2980.3430.3060.1230.1700.2690.1790.0000.0000.0000.0920.0000.0000.0000.0000.0000.0840.0000.0430.226
RoofMatl0.0910.0620.0320.3860.2180.0000.0000.0000.3590.0000.4240.3750.0640.0000.0400.0000.0070.0510.1250.1600.7351.0000.1520.2440.0000.0310.0740.0000.0000.0000.1560.2000.1040.0000.2390.2740.0000.0000.0000.0000.0000.0000.0000.1270.0000.0850.2900.1470.3940.0520.5490.1500.0670.0000.0560.1710.0760.1630.0000.0440.1540.2820.0000.1270.0000.3260.0000.0000.0000.0000.000NaN0.0000.2750.2390.0560.0000.0000.078
Exterior1st0.2260.4320.3720.3040.4340.0670.4380.2140.2270.0660.0880.7220.1980.0000.3460.3640.6360.4180.6550.6160.2180.1521.0000.9580.4090.1070.5790.2940.5540.6730.2970.3050.5430.2530.2330.1500.2120.2790.3450.4530.3420.3650.2410.2500.1390.2650.0740.2390.3530.3220.1250.2490.4950.1870.3260.2570.3530.4550.6240.5770.4000.3400.4120.2510.4160.1850.0610.1980.0000.0000.1490.0000.0000.0000.0000.0330.0000.2220.366
Exterior2nd0.2830.4940.4960.3860.2760.0000.5250.2160.1990.3610.0890.7990.1110.0000.5260.3850.5430.4180.6830.6480.3440.2440.9581.0000.3230.1410.5730.2380.5900.5510.2430.2460.4420.2400.2460.3010.2310.2850.6300.5470.3300.4080.2450.3910.1080.3020.0640.2610.4440.3700.1740.3410.4840.2310.1940.3100.3460.3830.6560.4780.4150.3670.2570.2650.4470.1820.0520.2070.0000.0000.0001.0000.0000.0000.0000.0650.0000.2700.374
MasVnrType0.1870.2300.0000.2210.0770.1880.0000.0470.1110.0700.2950.4680.4440.0000.1170.2260.2550.2570.3740.4690.0000.0000.4090.3231.0000.0000.1700.0000.3050.1730.0000.0290.2710.2510.0500.0550.1320.308NaN0.0900.1350.4040.2990.000NaN0.2130.0000.0000.0680.1760.0880.2630.2640.0790.0000.0420.3190.0000.2320.2250.2520.2990.0000.0000.5320.0000.1200.0570.0000.0000.0000.0000.3320.3910.0000.0000.0260.2470.426
MasVnrArea0.2540.1720.1200.3710.5900.0000.5310.1720.1750.0510.3650.5200.0590.0000.1690.1650.5900.1400.3040.2930.3280.0310.1070.1410.0001.0000.4530.0000.1910.4080.0570.2700.2220.5260.0000.1780.2090.5960.0000.2340.1790.0420.5520.2970.0000.5180.2330.0370.3330.2410.1010.0000.4030.5800.0000.4150.2260.2100.2380.3340.3510.5170.1560.0000.1970.2320.4170.0500.0000.1020.1221.0000.0000.0000.4360.1360.1010.0000.120
ExterQual0.2670.4180.2720.2340.2070.2510.8540.3640.3540.0220.0450.7470.2240.2040.2240.2840.7960.4570.6530.6140.2720.0740.5790.5730.1700.4531.0000.3360.5380.8270.4090.5120.4640.3750.1110.0000.2550.4560.2200.4110.4800.3390.4010.3150.0940.3960.3010.0560.3630.1990.0920.1190.8800.2720.1340.2140.2840.3350.4930.3730.5200.5480.3340.1620.2100.2450.2200.1480.1090.0800.0810.0000.1340.0730.0830.0260.0000.4050.360
ExterCond0.1230.1850.2590.0000.1190.0920.2640.0560.0990.0770.0870.3600.0980.0000.2740.0930.5890.5260.3900.2770.0000.0000.2940.2380.0000.0000.3361.0000.1920.1550.1750.0700.0940.0000.1000.1290.1590.0860.3500.6100.2260.1350.0000.1680.0000.0550.0550.0800.4520.1880.0000.0950.1370.0830.1210.2460.0000.1150.3070.1420.1790.1510.1340.4260.2490.1560.0860.2090.1260.0000.0750.0000.0000.0000.0000.1320.0540.1110.102
Foundation0.2730.4660.3740.2770.1640.2400.5110.2340.1430.0000.0000.7220.1420.0000.2370.3450.4980.4720.7330.5680.2660.0000.5540.5900.3050.1910.5380.1921.0000.4940.1830.1740.4130.2610.1500.0870.3190.4000.2560.4060.4530.2470.2460.3190.1650.3700.2050.1880.3640.4560.1120.3330.4870.2450.1820.1740.2600.5880.6140.7070.5730.3820.3480.1860.5350.1530.1920.1720.0000.1500.0000.0000.3050.0000.0000.0000.0000.2840.408
BsmtQual0.3710.4110.2730.3450.2270.1680.9300.4580.3750.1250.0520.7980.2230.1290.1890.3330.6910.4890.7300.6050.3380.0000.6730.5510.1730.4080.8270.1550.4941.0000.3870.5870.5240.3870.1240.0650.2500.4270.0170.3340.4090.3800.4110.4200.0000.5670.3890.0400.3790.2080.1380.0750.8200.3010.1130.2340.2210.4410.5110.4270.5740.5540.3400.1590.2830.2580.1950.1870.0000.0010.0000.0000.1210.0000.0290.0000.0120.3630.318
BsmtCond0.0570.1820.1530.0360.0000.1460.1960.1050.1290.0260.0270.3000.1470.0000.0920.1230.2050.2330.2870.1900.1570.1560.2970.2430.0000.0570.4090.1750.1830.3871.0000.1600.2000.0680.0450.0000.0950.0560.0000.1550.3070.2000.0470.1340.1160.0000.2330.0000.0860.0310.0660.0300.2700.0000.1300.0000.0000.1370.1790.1110.1690.1910.3080.1030.1750.0320.0000.1660.5140.0000.000NaN0.0000.0000.0000.0000.0000.0890.131
BsmtExposure0.1020.3340.1240.2340.2250.0000.0000.3280.4600.1010.1970.4750.1170.1160.0880.3400.3790.1840.3100.2850.2020.2000.3050.2460.0290.2700.5120.0700.1740.5870.1601.0000.3320.3670.0650.0780.1820.3080.0800.1060.1510.1610.3310.2530.0000.2460.4410.0880.1140.0120.1520.0440.4690.1670.0570.1760.1630.2540.1960.2390.2950.2960.1560.0000.1050.2220.1050.0870.0870.1290.0500.0000.0000.0000.0000.0400.0000.1290.136
BsmtFinType10.1980.3600.2190.2070.0740.0660.7820.1730.1460.1000.1730.6060.1370.0000.1810.2600.4630.3570.5800.4850.2100.1040.5430.4420.2710.2220.4640.0940.4130.5240.2000.3321.0000.5000.5130.3300.4850.2350.1000.2880.2820.1800.3570.2420.0800.2180.4990.2050.3010.2040.1510.1860.4170.1880.0870.1930.1750.3980.4440.5620.5180.3250.2070.1180.4650.2060.1250.1520.0000.1000.0001.0000.1080.0000.0180.0580.0240.2370.295
BsmtFinSF10.1030.2430.1730.2670.5320.0000.2080.1530.2030.0750.1530.4520.0940.2990.0890.3450.4610.1420.3240.2830.2060.0000.2530.2400.2510.5260.3750.0000.2610.3870.0680.3670.5001.0000.0560.0000.3870.9000.0000.1210.1470.0990.8990.2240.0000.6700.5050.1790.1660.0000.1590.0860.3540.6600.0830.2660.2560.2150.2390.2740.2960.4450.1910.0270.2260.3450.6450.0000.2050.1330.4591.0000.0000.0000.6360.0000.0000.1140.149
BsmtFinType20.0690.1330.0620.0000.0000.0000.0000.0000.1100.0480.2310.3490.0890.0000.0330.1250.1040.0830.2710.2610.2070.2390.2330.2460.0500.0000.1110.1000.1500.1240.0450.0650.5130.0561.0000.7220.2700.0000.0000.1170.0560.0000.0350.0970.0000.0990.1700.2770.1910.1430.2270.2330.0990.0700.0570.1420.1960.0410.1670.1380.1430.0000.0000.0000.0630.2000.0000.0580.0660.1190.2080.0000.0000.0000.0000.0000.0000.0600.028
BsmtFinSF20.0660.0520.0000.1630.0000.0000.0000.0000.1240.0840.2110.3400.0000.0000.0000.1070.0000.1120.1670.1640.1490.2740.1500.3010.0550.1780.0000.1290.0870.0650.0000.0780.3300.0000.7221.0000.1970.0000.0000.0000.0000.0000.1260.2150.0000.3980.1690.3310.2400.0130.2290.2120.0490.0840.0440.2300.1650.0000.1260.0000.1690.0540.0000.0000.0000.5040.0000.2680.0000.0770.1660.0000.0000.0000.0000.0000.0000.0000.000
BsmtUnfSF0.1130.3390.1960.1640.1870.0000.4610.0620.0000.1180.0880.4290.0770.1660.3580.3380.3100.1700.4390.3790.0830.0000.2120.2310.1320.2090.2550.1590.3190.2500.0950.1820.4850.3870.2700.1971.0000.5280.0000.2330.1230.0510.4520.2990.1260.2690.3640.0930.3340.1090.2130.2620.2360.3310.0000.0430.2110.1730.3670.2250.2640.3120.1140.0000.2210.0350.0970.1130.0000.0000.000NaN0.0480.0000.0000.1460.0000.2050.230
TotalBsmtSF0.1470.3900.2260.3500.5890.0240.4150.1650.1740.0660.0660.5260.1410.2500.1950.4980.5370.2320.3490.2920.2520.0000.2790.2850.3080.5960.4560.0860.4000.4270.0560.3080.2350.9000.0000.0000.5281.0000.0720.1740.2230.1000.9330.3810.0000.6960.2920.0790.2680.1380.2160.1580.4040.6790.2060.2660.2650.3070.2560.3650.3700.4840.2170.0460.2850.3230.6470.0000.0000.2660.2751.0000.0000.0000.6350.0830.0000.1960.188
Heating0.0000.1210.0000.2170.1900.0000.0000.0000.2050.0000.1490.0000.0000.0000.1150.0000.4660.1800.1670.0380.2490.0000.3450.630NaN0.0000.2200.3500.2560.0170.0000.0800.1000.0000.0000.0000.0000.0721.0000.3850.3940.3060.0000.1790.0000.0000.1830.0000.0000.0000.1510.1360.2480.5520.1500.0000.0000.1370.1210.1330.1240.1280.0560.2560.0930.0000.5770.0650.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.226
HeatingQC0.2380.3080.3640.0970.0830.0870.3130.1220.1140.0420.0550.6060.0470.0000.2960.1900.7020.2960.6620.6490.0540.0000.4530.5470.0900.2340.4110.6100.4060.3340.1550.1060.2880.1210.1170.0000.2330.1740.3851.0000.2650.2290.1510.2770.0000.2740.0630.0510.4440.2310.0690.1490.4180.2950.1000.2730.1860.1890.6650.3330.2890.3780.1330.3820.1990.1460.2200.0670.0000.1300.0000.0000.3620.0000.0000.0280.0000.2270.243
CentralAir0.1920.2650.2320.1410.1150.0590.3530.1950.1900.0520.0190.4220.1660.0000.2040.1370.5180.2970.5120.4950.2050.0000.3420.3300.1350.1790.4800.2260.4530.4090.3070.1510.2820.1470.0560.0000.1230.2230.3940.2651.0000.5460.1460.2610.0370.2210.1750.0200.1150.1000.1490.1140.3880.2460.1610.1330.0000.3510.2580.1430.5140.4900.5140.2540.2610.1080.1090.0930.0000.0000.000NaN0.0000.0000.0000.0160.0000.1380.369
Electrical0.1780.2640.2060.0800.0450.0000.4170.2350.1570.0000.0000.3380.1560.0000.1700.2280.3270.2270.3970.3800.0000.0000.3650.4080.4040.0420.3390.1350.2470.3800.2000.1610.1800.0990.0000.0000.0510.1000.3060.2290.5461.0000.0840.1010.0000.1340.1220.0000.1130.0560.0370.0550.3550.1770.0580.0810.0000.2120.2860.1760.2210.1920.3550.1640.1770.0830.0460.1660.0000.0540.000NaN0.1260.0000.0000.0000.0430.1200.172
1stFlrSF0.1300.3850.2250.3980.6650.0000.1960.1590.1620.0620.0810.5300.1080.1910.2020.4920.4970.2070.3080.2850.2630.0000.2410.2450.2990.5520.4010.0000.2460.4110.0470.3310.3570.8990.0350.1260.4520.9330.0000.1510.1460.0841.0000.4120.0000.8680.2870.2080.3030.1910.2340.0850.3720.6950.0000.3300.2350.2910.2120.3360.3450.4510.2310.1220.2330.3760.7060.0000.0000.1030.4010.0000.0680.0000.6350.0530.0500.1150.148
2ndFlrSF0.2280.6910.4700.2820.3540.0470.2430.2710.0720.0000.0000.6130.0690.0000.4010.7050.4750.2170.5390.4450.3000.1270.2500.3910.0000.2970.3150.1680.3190.4200.1340.2530.2420.2240.0970.2150.2990.3810.1790.2770.2610.1010.4121.0000.0000.6970.3350.0890.7750.6640.5160.2220.2940.6510.1460.3410.2980.4400.4580.3700.4520.5030.2010.2290.1690.3860.1700.0000.0000.0000.0000.0000.2130.0000.0000.1650.1430.0770.169
LowQualFinSF0.0000.3860.0000.0000.0000.0000.0000.0000.1560.0000.0000.0000.0000.0000.0000.2420.1160.1310.1860.0600.1250.0000.1390.108NaN0.0000.0940.0000.1650.0000.1160.0000.0800.0000.0000.0000.1260.0000.0000.0000.0370.0000.0000.0001.0000.0000.0000.0000.0000.0000.0990.0000.0000.1310.3740.0000.0000.0620.0420.0000.0000.0000.1570.0970.0980.0700.0000.6640.0000.0000.000NaN0.000NaN0.0000.0000.0410.2140.017
GrLivArea0.1560.5120.1650.5000.6310.1010.2200.2110.1310.0370.0000.5250.1620.0000.1560.3830.5160.3280.3290.2990.2260.0850.2650.3020.2130.5180.3960.0550.3700.5670.0000.2460.2180.6700.0990.3980.2690.6960.0000.2740.2210.1340.8680.6970.0001.0000.2040.1630.6100.6090.4840.1940.4140.7860.0000.4090.3580.3910.3270.5170.5380.4840.1630.1570.4250.4850.8390.0000.0000.0000.079NaN0.1530.0000.7060.1100.0440.1590.160
BsmtFullBath0.0770.3890.1130.1630.1100.0000.4310.1120.1340.0340.0990.3420.0000.0670.2720.2570.2610.0860.2340.2020.1910.2900.0740.0640.0000.2330.3010.0550.2050.3890.2330.4410.4990.5050.1700.1690.3640.2920.1830.0630.1750.1220.2870.3350.0000.2041.0000.1110.3510.2120.3580.2540.3050.1280.0330.1560.0000.3240.1550.4250.3710.2390.1240.0980.1150.2660.1860.0000.0000.0000.1551.0000.0000.0000.0000.0000.0000.0270.270
BsmtHalfBath0.0000.3110.0000.0900.0000.0000.0000.0210.0990.0720.1690.3120.0000.0000.0780.2030.0000.1260.2050.2310.4140.1470.2390.2610.0000.0370.0560.0800.1880.0400.0000.0880.2050.1790.2770.3310.0930.0790.0000.0510.0200.0000.2080.0890.0000.1630.1111.0000.0000.1990.0000.7000.0530.1590.1400.5550.1150.0000.0910.0000.1780.0990.0000.0000.0590.3000.1230.0000.0000.0000.6030.0000.0350.0000.1070.0400.0140.0000.379
FullBath0.3440.4010.3510.3020.2900.0390.2550.1800.1080.2320.2970.6360.1580.0000.4150.2800.5880.3790.6250.5510.2440.3940.3530.4440.0680.3330.3630.4520.3640.3790.0860.1140.3010.1660.1910.2400.3340.2680.0000.4440.1150.1130.3030.7750.0000.6100.3510.0001.0000.4870.6490.2730.3180.5860.0000.4160.3010.2990.6350.3880.4960.5340.1220.0750.1650.2840.2240.0390.0000.0000.0000.0000.1640.0000.0000.1380.0970.2020.205
HalfBath0.2420.7980.1430.2850.1300.0000.6430.1280.0000.0860.0000.5460.2240.0000.3230.5620.3520.3590.3780.3430.2430.0520.3220.3700.1760.2410.1990.1880.4560.2080.0310.0120.2040.0000.1430.0130.1090.1380.0000.2310.1000.0560.1910.6640.0000.6090.2120.1990.4871.0000.4180.5360.1950.4140.0000.2290.1920.4690.2760.4560.5190.2490.0590.0880.3260.1770.2150.0640.0000.0000.0000.0000.0860.4150.0000.0660.0370.1160.205
BedroomAbvGr0.1000.5230.1620.3180.2930.0770.3640.1560.1410.1700.4090.4270.1000.0000.4590.5250.2430.0340.1590.1810.3050.5490.1250.1740.0880.1010.0920.0000.1120.1380.0660.1520.1510.1590.2270.2290.2130.2160.1510.0690.1490.0370.2340.5160.0990.4840.3580.0000.6490.4181.0000.4600.1310.6200.1590.1030.1730.1790.0280.0770.2420.1900.0000.0000.1390.3550.2320.0000.0590.0000.0661.0000.0980.0000.0000.0890.0000.0930.060
KitchenAbvGr0.1160.8940.0700.1030.0000.0000.0000.0580.0000.0490.0000.2080.0000.0000.6620.1510.1420.1290.1570.2250.4010.1500.2490.3410.2630.0000.1190.0950.3330.0750.0300.0440.1860.0860.2330.2120.2620.1580.1360.1490.1140.0550.0850.2220.0000.1940.2540.7000.2730.5360.4601.0000.1350.4510.1040.1500.0000.2440.0840.2490.4930.2730.0000.0810.2020.0320.1830.0000.0000.0000.000NaN0.0000.0000.0000.0630.0100.0780.472
KitchenQual0.2540.3670.2110.2730.2500.1040.6610.3270.3330.0000.0460.6960.1680.1430.1960.2360.7500.3510.5890.6110.2810.0670.4950.4840.2640.4030.8800.1370.4870.8200.2700.4690.4170.3540.0990.0490.2360.4040.2480.4180.3880.3550.3720.2940.0000.4140.3050.0530.3180.1950.1310.1351.0000.3290.0900.2020.2560.3260.4310.3410.4980.4810.2950.1750.1880.2460.1990.1130.0000.0000.0270.0000.0960.0000.0730.1130.0470.3380.331
TotRmsAbvGrd0.2300.4540.1580.4220.6740.0000.0920.1500.2010.1350.0410.4290.0870.0390.4890.4160.5030.1530.3380.2950.0950.0000.1870.2310.0790.5800.2720.0830.2450.3010.0000.1670.1880.6600.0700.0840.3310.6790.5520.2950.2460.1770.6950.6510.1310.7860.1280.1590.5860.4140.6200.4510.3291.0000.1990.4110.3680.2850.3050.3160.3880.4990.0590.0200.1830.2800.6920.0000.0000.0000.0001.0000.1570.0000.6790.1900.1290.1340.197
Functional0.0300.1420.0450.1650.3480.0540.0000.0000.1890.0000.0510.1380.0000.0000.1060.1790.2790.4810.2080.1370.0340.0560.3260.1940.0000.0000.1340.1210.1820.1130.1300.0570.0870.0830.0570.0440.0000.2060.1500.1000.1610.0580.0000.1460.3740.0000.0330.1400.0000.0000.1590.1040.0900.1991.0000.1830.0000.2330.1750.1880.0800.1120.0750.1460.2170.2760.1830.3360.3290.0000.3680.0000.0000.0000.0000.0820.0000.0000.091
Fireplaces0.2270.2930.2820.2830.4040.0000.0000.1480.1880.1340.1300.5600.1420.0450.2900.1600.4710.2020.3960.3250.1390.1710.2570.3100.0420.4150.2140.2460.1740.2340.0000.1760.1930.2660.1420.2300.0430.2660.0000.2730.1330.0810.3300.3410.0000.4090.1560.5550.4160.2290.1030.1500.2020.4110.1831.0000.0000.2560.3150.2930.2840.3920.0980.1420.1850.3650.0990.3050.0000.1660.6571.0000.2170.0000.0070.0000.0660.1320.096
FireplaceQu0.0000.3360.0700.2220.1070.0000.0000.0000.0970.1210.0580.5920.1380.0000.0520.3050.3010.1620.5830.6770.1180.0760.3530.3460.3190.2260.2840.0000.2600.2210.0000.1630.1750.2560.1960.1650.2110.2650.0000.1860.0000.0000.2350.2980.0000.3580.0000.1150.3010.1920.1730.0000.2560.3680.0000.0001.0000.2260.4870.1430.3760.3350.0000.0400.0780.0830.1520.1210.1830.2130.1440.0000.0000.0000.0000.0000.0000.2680.367
GarageType0.2400.5310.3200.4690.3500.0480.3650.2820.0910.0820.1700.6270.1590.0220.1940.4040.3860.3470.5110.3950.3360.1630.4550.3830.0000.2100.3350.1150.5880.4410.1370.2540.3980.2150.0410.0000.1730.3070.1370.1890.3510.2120.2910.4400.0620.3910.3240.0000.2990.4690.1790.2440.3260.2850.2330.2560.2261.0000.3350.7860.3060.3170.2450.1690.5590.1490.1360.1010.0000.0000.148NaN0.0000.0000.0000.0000.0060.2030.311
GarageYrBlt0.3890.4540.5550.2660.2350.0710.8730.2180.1570.1690.0000.8020.1770.0880.3670.3690.6660.4870.9390.8220.0850.0000.6240.6560.2320.2380.4930.3070.6140.5110.1790.1960.4440.2390.1670.1260.3670.2560.1210.6650.2580.2860.2120.4580.0420.3270.1550.0910.6350.2760.0280.0840.4310.3050.1750.3150.4870.3351.0000.4560.6910.6220.3250.4840.3630.2090.1550.2300.0000.0000.0000.0000.3140.0000.0000.0980.0440.2950.276
GarageFinish0.3480.5770.2930.3810.2330.0140.2970.2260.1360.1020.0000.7060.2920.0780.2090.3350.5400.5610.6140.5000.2980.0440.5770.4780.2250.3340.3730.1420.7070.4270.1110.2390.5620.2740.1380.0000.2250.3650.1330.3330.1430.1760.3360.3700.0000.5170.4250.0000.3880.4560.0770.2490.3410.3160.1880.2930.1430.7860.4561.0000.3980.4420.1820.1840.5020.2520.2530.1640.0500.1760.0460.0000.0610.2530.0630.0960.0200.4230.439
GarageCars0.2380.4390.2790.4260.3020.0000.3150.2630.1150.0920.0000.6680.1380.0730.2960.2390.5890.3940.5170.4260.3430.1540.4000.4150.2520.3510.5200.1790.5730.5740.1690.2950.5180.2960.1430.1690.2640.3700.1240.2890.5140.2210.3450.4520.0000.5380.3710.1780.4960.5190.2420.4930.4980.3880.0800.2840.3760.3060.6910.3981.0000.8670.1770.2130.6110.2470.1450.0700.0000.0000.2080.0000.1110.0000.0000.0990.0000.2630.401
GarageArea0.2550.3380.4320.3540.4720.0000.4190.2530.1010.1550.0000.6210.1310.0000.3900.2180.6760.3110.5530.4810.3060.2820.3400.3670.2990.5170.5480.1510.3820.5540.1910.2960.3250.4450.0000.0540.3120.4840.1280.3780.4900.1920.4510.5030.0000.4840.2390.0990.5340.2490.1900.2730.4810.4990.1120.3920.3350.3170.6220.4420.8671.0000.3870.3510.4490.2490.2910.0000.0760.2330.0781.0000.1060.0000.2580.1440.0000.1910.268
GarageQual0.1690.3600.2540.1370.2200.0000.3580.1430.1350.0000.0000.4200.0000.0000.0310.2140.3280.2070.4190.2980.1230.0000.4120.2570.0000.1560.3340.1340.3480.3400.3080.1560.2070.1910.0000.0000.1140.2170.0560.1330.5140.3550.2310.2010.1570.1630.1240.0000.1220.0590.0000.0000.2950.0590.0750.0980.0000.2450.3250.1820.1770.3871.0000.4180.2600.0000.0000.1240.0000.2240.2450.0000.0000.0000.0000.0000.0450.1170.000
GarageCond0.0720.1310.3350.0000.0780.0260.2030.0410.0640.0000.0000.2970.0000.0000.1040.1170.4910.3330.3710.2700.1700.1270.2510.2650.0000.0000.1620.4260.1860.1590.1030.0000.1180.0270.0000.0000.0000.0460.2560.3820.2540.1640.1220.2290.0970.1570.0980.0000.0750.0880.0000.0810.1750.0200.1460.1420.0400.1690.4840.1840.2130.3510.4181.0000.3670.0000.0000.0000.2800.0000.2950.0000.0000.0000.0000.1410.0000.2200.037
PavedDrive0.2420.3530.3340.3590.2590.1100.2240.1420.0410.0530.0000.6190.2900.0000.1570.2440.4850.5460.5620.4120.2690.0000.4160.4470.5320.1970.2100.2490.5350.2830.1750.1050.4650.2260.0630.0000.2210.2850.0930.1990.2610.1770.2330.1690.0980.4250.1150.0590.1650.3260.1390.2020.1880.1830.2170.1850.0780.5590.3630.5020.6110.4490.2600.3671.0000.1440.0000.3050.0000.0000.181NaN0.0000.0000.0000.0000.0000.2450.248
WoodDeckSF0.1030.1820.1090.2880.2910.0000.0000.1210.2280.1040.2600.3650.0570.0000.0720.2400.2730.1410.2580.2620.1790.3260.1850.1820.0000.2320.2450.1560.1530.2580.0320.2220.2060.3450.2000.5040.0350.3230.0000.1460.1080.0830.3760.3860.0700.4850.2660.3000.2840.1770.3550.0320.2460.2800.2760.3650.0830.1490.2090.2520.2470.2490.0000.0000.1441.0000.1470.0000.1340.1060.4660.0000.1410.0000.1510.0400.0000.0000.000
OpenPorchSF0.0000.2210.2170.3500.5360.0000.3940.0510.0730.0000.0000.2980.0000.0220.1030.0990.2510.1640.2010.1670.0000.0000.0610.0520.1200.4170.2200.0860.1920.1950.0000.1050.1250.6450.0000.0000.0970.6470.5770.2200.1090.0460.7060.1700.0000.8390.1860.1230.2240.2150.2320.1830.1990.6920.1830.0990.1520.1360.1550.2530.1450.2910.0000.0000.0000.1471.0000.0910.0000.0000.1010.0000.0000.6440.7310.0620.0000.0000.317
EnclosedPorch0.1070.1200.1490.0480.3820.0910.2460.0430.0000.0000.0000.2680.0780.0000.1110.1820.0820.1450.3090.2340.0000.0000.1980.2070.0570.0500.1480.2090.1720.1870.1660.0870.1520.0000.0580.2680.1130.0000.0650.0670.0930.1660.0000.0000.6640.0000.0000.0000.0390.0640.0000.0000.1130.0000.3360.3050.1210.1010.2300.1640.0700.0000.1240.0000.3050.0000.0911.0000.0000.0000.8090.0000.0000.3670.0000.0000.0000.0000.125
3SsnPorch0.0000.0520.2060.0000.0000.0000.0000.0000.2420.0000.0490.2750.0000.0000.0590.0000.0000.1770.0000.0830.0000.0000.0000.0000.0000.0000.1090.1260.0000.0000.5140.0870.0000.2050.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.3290.0000.1830.0000.0000.0500.0000.0760.0000.2800.0000.1340.0000.0001.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.000
ScreenPorch0.0610.1120.0000.0890.3530.0000.0000.0000.1600.0270.1330.1510.2470.0000.0000.0000.1200.0000.0190.1430.0920.0000.0000.0000.0000.1020.0800.0000.1500.0010.0000.1290.1000.1330.1190.0770.0000.2660.0000.1300.0000.0540.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1660.2130.0000.0000.1760.0000.2330.2240.0000.0000.1060.0000.0000.0001.0000.0000.0000.0000.7990.0000.0000.0000.0000.000
PoolArea0.0030.0000.0000.4950.5570.000NaN0.0000.1980.0000.0940.1910.0240.0000.0000.0000.2590.0000.0000.1210.0000.0000.1490.0000.0000.1220.0810.0750.0000.0000.0000.0500.0000.4590.2080.1660.0000.2750.0000.0000.0000.0000.4010.0000.0000.0790.1550.6030.0000.0000.0660.0000.0270.0000.3680.6570.1440.1480.0000.0460.2080.0780.2450.2950.1810.4660.1010.8090.0000.0001.0001.0000.145NaN0.0000.0000.0000.0000.000
PoolQC1.0000.000NaN0.0000.000NaNNaN0.0000.0001.0000.0001.000NaNNaNNaN0.0001.0000.0001.0001.0000.000NaN0.0001.0000.0001.0000.0000.0000.0000.000NaN0.0001.0001.0000.0000.000NaN1.000NaN0.000NaNNaN0.0000.000NaNNaN1.0000.0000.0000.0001.000NaN0.0001.0000.0001.0000.000NaN0.0000.0000.0001.0000.0000.000NaN0.0000.0000.000NaN0.0001.0001.000NaNNaNNaN0.0001.000NaNNaN
Fence0.0000.1650.1290.0000.0000.0000.0000.2100.0000.0180.0000.3340.0000.0000.0000.0600.0600.0000.2700.1900.0000.0000.0000.0000.3320.0000.1340.0000.3050.1210.0000.0000.1080.0000.0000.0000.0480.0000.0000.3620.0000.1260.0680.2130.0000.1530.0000.0350.1640.0860.0980.0000.0960.1570.0000.2170.0000.0000.3140.0610.1110.1060.0000.0000.0000.1410.0000.0000.0000.0000.145NaN1.0000.0000.0000.0000.0000.0000.000
MiscFeature0.0000.0000.0000.0000.000NaN0.0000.6370.0000.0000.3380.0000.0000.0000.0000.0000.0000.0000.0000.4980.0000.2750.0000.0000.3910.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.4150.0000.0000.0000.0000.0000.0000.0000.0000.0000.2530.0000.0000.0000.0000.0000.0000.6440.3670.0000.799NaNNaN0.0001.0000.7240.4150.0000.0000.454
MiscVal0.0200.0000.0000.0760.5240.0000.0000.0950.0900.0000.0000.0000.0000.0000.0000.0000.2110.0000.0000.0000.0000.2390.0000.0000.0000.4360.0830.0000.0000.0290.0000.0000.0180.6360.0000.0000.0000.6350.0000.0000.0000.0000.6350.0000.0000.7060.0000.1070.0000.0000.0000.0000.0730.6790.0000.0070.0000.0000.0000.0630.0000.2580.0000.0000.0000.1510.7310.0000.0000.0000.000NaN0.0000.7241.0000.0000.0000.0000.000
MoSold0.2720.0280.0000.0000.1780.0000.2490.0670.0000.0690.0250.1550.0600.0660.0000.0450.1390.0570.0000.0680.0840.0560.0330.0650.0000.1360.0260.1320.0000.0000.0000.0400.0580.0000.0000.0000.1460.0830.0000.0280.0160.0000.0530.1650.0000.1100.0000.0400.1380.0660.0890.0630.1130.1900.0820.0000.0000.0000.0980.0960.0990.1440.0000.1410.0000.0400.0620.0000.0000.0000.0000.0000.0000.4150.0001.0000.3430.1140.170
YrSold0.9970.0960.1670.0420.0860.0000.1120.0000.0000.0000.0510.1770.0580.0000.0000.0570.0840.0610.0000.0880.0000.0000.0000.0000.0260.1010.0000.0540.0000.0120.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0430.0500.1430.0410.0440.0000.0140.0970.0370.0000.0100.0470.1290.0000.0660.0000.0060.0440.0200.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.3431.0000.2100.144
SaleType0.2050.2270.1530.0000.0000.0600.2860.1030.0460.0000.0000.3440.1360.0000.1120.1250.3630.2710.3540.3900.0430.0000.2220.2700.2470.0000.4050.1110.2840.3630.0890.1290.2370.1140.0600.0000.2050.1960.0000.2270.1380.1200.1150.0770.2140.1590.0270.0000.2020.1160.0930.0780.3380.1340.0000.1320.2680.2030.2950.4230.2630.1910.1170.2200.2450.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.1140.2101.0000.737
SaleCondition0.2320.2950.1620.1510.1830.3130.2550.1170.1240.0000.0340.4100.1330.0750.2110.1230.3210.2720.3720.4270.2260.0780.3660.3740.4260.1200.3600.1020.4080.3180.1310.1360.2950.1490.0280.0000.2300.1880.2260.2430.3690.1720.1480.1690.0170.1600.2700.3790.2050.2050.0600.4720.3310.1970.0910.0960.3670.3110.2760.4390.4010.2680.0000.0370.2480.0000.3170.1250.0000.0000.000NaN0.0000.4540.0000.1700.1440.7371.000
2025-06-03T13:16:58.832460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AlleyBldgTypeBsmtCondBsmtExposureBsmtFinType1BsmtFinType2BsmtFullBathBsmtHalfBathBsmtQualCentralAirCondition1Condition2ElectricalExterCondExterQualExterior1stExterior2ndFenceFireplaceQuFireplacesFoundationFullBathFunctionalGarageCondGarageFinishGarageQualGarageTypeHalfBathHeatingHeatingQCHouseStyleKitchenAbvGrKitchenQualLandContourLandSlopeLotConfigLotShapeMSZoningMasVnrTypeMiscFeatureNeighborhoodPavedDriveRoofMatlRoofStyleSaleConditionSaleTypeStreetYrSold
Alley1.0000.6860.1280.0000.5760.0000.2830.0000.7530.2290.1551.0000.2770.1730.6470.4010.3880.0000.0000.0000.6100.4130.0000.1300.4770.2330.4380.4450.0000.3770.3330.0000.4580.0410.0000.0000.4450.8480.0000.0000.8960.3640.0000.0000.3070.3441.0000.134
BldgType0.6861.0000.0750.0720.1230.0220.2250.0580.1550.2490.0720.0000.1390.1050.1840.1950.2510.0000.0190.1120.1620.1650.0680.0390.1600.0260.1320.2580.0940.1140.1560.6320.1610.0760.0570.0930.0950.2030.0870.0000.4330.1190.0320.0460.1440.0640.0000.000
BsmtCond0.1280.0751.0000.0640.1300.0290.0930.0000.1590.2040.0940.0000.0800.1440.1690.1410.1430.0000.0000.0000.1500.0700.0890.0840.1050.1240.0880.0290.0000.1270.0840.0280.1090.0510.0250.0210.0420.1250.0000.0000.1620.1650.0620.1020.0850.0570.0970.000
BsmtExposure0.0000.0720.0641.0000.2190.0420.1840.0830.2630.1000.0750.0940.0640.0570.2200.1450.1450.0000.1330.1440.1420.0930.0390.0000.2280.0620.1660.0120.0760.0870.2390.0420.1970.1930.1870.0830.1330.1010.0270.0000.2680.0990.0800.1320.0880.0830.0000.000
BsmtFinType10.5760.1230.1300.2191.0000.2060.3440.0860.3640.2030.0680.0000.1170.0640.3160.2420.2360.0690.1190.1310.2950.2090.0520.0800.2770.1340.1530.0860.0410.2000.1580.0780.2800.0940.0720.0680.1120.1500.1160.0000.3210.2170.0670.0770.1110.1200.0470.016
BsmtFinType20.0000.0220.0290.0420.2061.0000.1100.1190.0800.0410.0440.0000.0000.0670.0720.0930.1240.0000.1330.0960.1020.1300.0340.0000.0570.0000.0150.0590.0000.0790.0740.0990.0640.0710.0980.0320.0000.0420.0200.0000.1630.0260.1560.0760.0100.0300.0000.000
BsmtFullBath0.2830.2250.0930.1840.3440.1101.0000.1050.1600.1160.0000.0540.0490.0450.1210.0430.0360.0000.0000.1270.1330.2920.0230.0730.1600.1170.1420.2020.0730.0510.1790.2420.1230.0530.0940.0270.0450.0920.0000.0000.1850.1090.1170.1240.1770.0170.0000.000
BsmtHalfBath0.0000.0580.0000.0830.0860.1190.1051.0000.0380.0320.0000.0000.0000.0600.0530.1370.1220.0320.0860.4980.0790.0000.0940.0000.0000.0000.0000.0620.0000.0380.1390.3570.0500.0930.0520.0540.0190.0000.0000.0000.1670.0170.1390.1880.1690.0000.0000.011
BsmtQual0.7530.1550.1590.2630.3640.0800.1600.0381.0000.2750.1440.1050.1560.1270.4730.3690.3530.0480.1820.1930.4230.3180.0780.1300.4200.1380.2980.1970.0160.2780.2340.0710.4640.1540.0490.1020.1920.2250.1630.0000.5590.2710.0000.2230.2100.2390.1110.010
CentralAir0.2290.2490.2040.1000.2030.0410.1160.0320.2751.0000.1650.0000.3730.2760.3250.3180.3000.0000.0000.1630.3260.1410.1720.3100.2360.3490.2520.1650.2640.3230.1460.1890.2600.1260.0320.0630.1290.2830.2230.0000.3620.4240.0000.1470.2650.1380.0380.000
Condition10.1550.0720.0940.0750.0680.0440.0000.0000.1440.1651.0000.3650.1000.0560.1440.0860.0450.0000.0800.0820.0710.0910.0000.0000.1340.0000.0790.1000.0000.0270.0470.0000.1080.0640.0000.1130.0960.0910.2180.0000.1920.1330.0410.0000.0660.0440.1890.033
Condition21.0000.0000.0000.0940.0000.0000.0540.0000.1050.0000.3651.0000.0000.0000.1670.0000.0000.0000.0000.0170.0000.0000.0000.0000.0580.0000.0150.0000.0000.0000.0000.0000.1170.0940.0000.0350.0740.0000.0000.0000.1210.0000.0000.0290.0500.0000.0000.000
Electrical0.2770.1390.0800.0640.1170.0000.0490.0000.1560.3730.1000.0001.0000.1100.1380.2200.2420.1180.0000.0660.1610.0930.0400.1340.1670.1450.1380.0530.1240.1880.1580.0520.1450.0630.0000.0000.0940.1690.1490.0000.1830.1670.0000.0000.1120.0770.0000.035
ExterCond0.1730.1050.1440.0570.0640.0670.0450.0600.1270.2760.0560.0000.1101.0000.2800.1630.1030.0000.0000.0940.1310.1830.0770.1710.1070.1090.0780.1430.2920.2670.0590.0710.1120.0810.0650.0290.0460.0990.0000.0000.1620.1930.0000.0000.0690.0640.1120.020
ExterQual0.6470.1840.1690.2200.3160.0720.1210.0530.4730.3250.1440.1670.1380.2801.0000.3770.3620.0530.2350.1760.3760.3030.0920.1330.3620.1360.2210.1880.0880.3460.1980.1120.5520.1440.0420.0180.1490.2250.1610.1160.4990.2000.0290.1780.2390.2700.1670.000
Exterior1st0.4010.1950.1410.1450.2420.0930.0430.1370.3690.3180.0860.0000.2200.1630.3771.0000.7870.0000.2020.1410.3130.1990.1570.1410.3160.2000.1940.1910.2070.2650.1780.1440.3100.1340.0490.0350.1260.2150.2680.0000.3220.2590.0890.1090.1900.0970.0620.000
Exterior2nd0.3880.2510.1430.1450.2360.1240.0360.1220.3530.3000.0450.0000.2420.1030.3620.7871.0000.0000.1980.1370.3220.2040.0890.1410.3030.1470.1980.1810.4110.2640.1850.1650.2940.1140.0400.1610.1240.2330.2030.0000.3760.2280.1400.1680.1840.1130.0000.000
Fence0.0000.0000.0000.0000.0690.0000.0000.0320.0480.0000.0000.0000.1180.0000.0530.0000.0001.0000.0000.0860.1230.1550.0000.0000.0570.0000.0000.0810.0000.1480.0400.0000.0380.0000.0000.0130.0840.0510.1130.0000.1770.0000.0000.0000.0000.0000.0000.000
FireplaceQu0.0000.0190.0000.1330.1190.1330.0000.0860.1820.0000.0800.0000.0000.0000.2350.2020.1980.0001.0000.0000.2150.1160.0000.0290.1070.0000.0860.1460.0000.1520.2120.0000.2110.0800.0430.0460.0000.0260.2540.0000.2950.0590.0620.0800.1440.1750.0000.000
Fireplaces0.0000.1120.0000.1440.1310.0960.1270.4980.1930.1630.0820.0170.0660.0940.1760.1410.1370.0860.0001.0000.1190.1660.1170.0540.2310.0800.1760.1760.0000.1050.1020.1130.1660.1550.0980.0500.1210.1080.0310.0000.2760.1410.1400.0940.0650.0760.0000.025
Foundation0.6100.1620.1500.1420.2950.1020.1330.0790.4230.3260.0710.0000.1610.1310.3760.3130.3220.1230.2150.1191.0000.2570.1090.1270.3880.2300.2470.2110.1670.2900.2130.1460.3340.0920.0000.0000.1520.2640.2420.0000.4230.2590.0000.0990.1580.1450.1720.000
FullBath0.4130.1650.0700.0930.2090.1300.2920.0000.3180.1410.0910.0000.0930.1830.3030.1990.2040.1550.1160.1660.2571.0000.0000.0280.3190.0990.2070.4210.0000.1790.1830.2140.2640.0880.2340.0880.1480.1370.0500.0000.3280.1250.3300.1680.1400.1180.0480.036
Functional0.0000.0680.0890.0390.0520.0340.0230.0940.0780.1720.0000.0000.0400.0770.0920.1570.0890.0000.0000.1170.1090.0001.0000.0990.0790.0480.0860.0000.1030.0640.0630.0700.0610.1310.0340.0000.0000.0280.0000.0000.0580.1490.0390.0200.0540.0000.0570.000
GarageCond0.1300.0390.0840.0000.0800.0000.0730.0000.1300.3100.0000.0000.1340.1710.1330.1410.1410.0000.0290.0540.1270.0280.0991.0000.1400.3520.1150.0660.1990.1510.0740.0610.1440.0520.0000.0000.0340.1300.0000.0000.1310.2980.1040.1150.0250.1290.0320.000
GarageFinish0.4770.1600.1050.2280.2770.0570.1600.0000.4200.2360.1340.0580.1670.1070.3620.3160.3030.0570.1070.2310.3880.3190.0790.1401.0000.1730.4650.1770.0400.2670.2390.0810.3300.1280.0000.0770.2150.2310.0720.0750.4870.2040.0410.1290.2020.2060.0240.015
GarageQual0.2330.0260.1240.0620.1340.0000.1170.0000.1380.3490.0000.0000.1450.1090.1360.2000.1470.0000.0000.0800.2300.0990.0480.3520.1731.0000.1600.0560.0520.1090.1480.0000.1190.0540.0000.0000.0570.2090.0000.0000.2320.2480.0000.0800.0000.0750.0000.037
GarageType0.4380.1320.0880.1660.1530.0150.1420.0000.2980.2520.0790.0150.1380.0780.2210.1940.1980.0000.0860.1760.2470.2070.0860.1150.4650.1601.0000.2190.0570.1290.2540.1040.2150.0580.0710.0550.1850.2230.0000.0000.3370.2750.1050.1270.1170.1020.0340.004
HalfBath0.4450.2580.0290.0120.0860.0590.2020.0620.1970.1650.1000.0000.0530.1430.1880.1910.1810.0810.1460.1760.2110.4210.0000.0660.1770.0560.2191.0000.0000.1780.4510.2260.1850.0000.0000.0640.1210.1080.0540.1520.3340.1120.0490.1030.0860.0510.0000.028
Heating0.0000.0940.0000.0760.0410.0000.0730.0000.0160.2640.0000.0000.1240.2920.0880.2070.4110.0000.0000.0000.1670.0000.1030.1990.0400.0520.0570.0001.0000.3230.0000.1280.1000.0820.1410.0000.0000.0001.0000.0000.0000.0880.0000.1630.1470.0000.0000.000
HeatingQC0.3770.1140.1270.0870.2000.0790.0510.0380.2780.3230.0270.0000.1880.2670.3460.2650.2640.1480.1520.1050.2900.1790.0640.1510.2670.1090.1290.1780.3231.0000.1220.1130.3520.0930.0410.0150.1000.1430.0850.0000.3060.1520.0000.0360.1670.1330.1060.000
HouseStyle0.3330.1560.0840.2390.1580.0740.1790.1390.2340.1460.0470.0000.1580.0590.1980.1780.1850.0400.2120.1020.2130.1830.0630.0740.2390.1480.2540.4510.0000.1221.0000.1020.1640.0850.0410.0480.1270.1780.0950.0000.2960.1680.0000.1100.0730.0660.0000.036
KitchenAbvGr0.0000.6320.0280.0420.0780.0990.2420.3570.0710.1890.0000.0000.0520.0710.1120.1440.1650.0000.0000.1130.1460.2140.0700.0610.0810.0000.1040.2260.1280.1130.1021.0000.1280.0000.0000.0360.0550.0520.0860.0000.1070.0630.1410.1810.2210.0340.0000.007
KitchenQual0.4580.1610.1090.1970.2800.0640.1230.0500.4640.2600.1080.1170.1450.1120.5520.3100.2940.0380.2110.1660.3340.2640.0610.1440.3300.1190.2150.1850.1000.3520.1640.1281.0000.1350.0430.0000.1330.1740.2520.0000.4450.1780.0270.1840.2180.2220.0690.038
LandContour0.0410.0760.0510.1930.0940.0710.0530.0930.1540.1260.0640.0940.0630.0810.1440.1340.1140.0000.0800.1550.0920.0880.1310.0520.1280.0540.0580.0000.0820.0930.0850.0000.1351.0000.4020.0000.1090.0360.1040.0000.3680.0380.1470.1150.0800.0300.0910.000
LandSlope0.0000.0570.0250.1870.0720.0980.0940.0520.0490.0320.0000.0000.0000.0650.0420.0490.0400.0000.0430.0980.0000.2340.0340.0000.0000.0000.0710.0000.1410.0410.0410.0000.0430.4021.0000.0000.0340.0000.0990.5350.2070.0000.4170.2410.0140.0000.0000.038
LotConfig0.0000.0930.0210.0830.0680.0320.0270.0540.1020.0630.1130.0350.0000.0290.0180.0350.1610.0130.0460.0500.0000.0880.0000.0000.0770.0000.0550.0640.0000.0150.0480.0360.0000.0000.0001.0000.2140.0960.0520.0000.1660.0400.0000.0000.0000.0000.0000.000
LotShape0.4450.0950.0420.1330.1120.0000.0450.0190.1920.1290.0960.0740.0940.0460.1490.1260.1240.0840.0000.1210.1520.1480.0000.0340.2150.0570.1850.1210.0000.1000.1270.0550.1330.1090.0340.2141.0000.1580.0440.3000.2750.1340.0000.0000.0750.0660.0000.000
MSZoning0.8480.2030.1250.1010.1500.0420.0920.0000.2250.2830.0910.0000.1690.0990.2250.2150.2330.0510.0260.1080.2640.1370.0280.1300.2310.2090.2230.1080.0000.1430.1780.0520.1740.0360.0000.0960.1581.0000.0000.0000.6510.2680.0260.1030.1100.0880.3400.063
MasVnrType0.0000.0870.0000.0270.1160.0200.0000.0000.1630.2230.2180.0000.1490.0000.1610.2680.2030.1130.2540.0310.2420.0500.0000.0000.0720.0000.0000.0541.0000.0850.0950.0860.2520.1040.0990.0520.0440.0001.0000.5880.2720.2230.0000.0000.1940.1610.3080.019
MiscFeature0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.1520.0000.0000.0000.0000.0000.0000.5350.0000.3000.0000.5881.0000.0000.0000.4410.0000.4430.0001.0000.000
Neighborhood0.8960.4330.1620.2680.3210.1630.1850.1670.5590.3620.1920.1210.1830.1620.4990.3220.3760.1770.2950.2760.4230.3280.0580.1310.4870.2320.3370.3340.0000.3060.2960.1070.4450.3680.2070.1660.2750.6510.2720.0001.0000.3980.2050.2120.1960.1380.2370.076
PavedDrive0.3640.1190.1650.0990.2170.0260.1090.0170.2710.4240.1330.0000.1670.1930.2000.2590.2280.0000.0590.1410.2590.1250.1490.2980.2040.2480.2750.1120.0880.1520.1680.0630.1780.0380.0000.0400.1340.2680.2230.0000.3981.0000.0000.1150.1060.1100.1820.000
RoofMatl0.0000.0320.0620.0800.0670.1560.1170.1390.0000.0000.0410.0000.0000.0000.0290.0890.1400.0000.0620.1400.0000.3300.0390.1040.0410.0000.1050.0490.0000.0000.0000.1410.0270.1470.4170.0000.0000.0260.0000.4410.2050.0001.0000.5700.0500.0000.0000.000
RoofStyle0.0000.0460.1020.1320.0770.0760.1240.1880.2230.1470.0000.0290.0000.0000.1780.1090.1680.0000.0800.0940.0990.1680.0200.1150.1290.0800.1270.1030.1630.0360.1100.1810.1840.1150.2410.0000.0000.1030.0000.0000.2120.1150.5701.0000.0830.0210.0000.000
SaleCondition0.3070.1440.0850.0880.1110.0100.1770.1690.2100.2650.0660.0500.1120.0690.2390.1900.1840.0000.1440.0650.1580.1400.0540.0250.2020.0000.1170.0860.1470.1670.0730.2210.2180.0800.0140.0000.0750.1100.1940.4430.1960.1060.0500.0831.0000.4750.2250.097
SaleType0.3440.0640.0570.0830.1200.0300.0170.0000.2390.1380.0440.0000.0770.0640.2700.0970.1130.0000.1750.0760.1450.1180.0000.1290.2060.0750.1020.0510.0000.1330.0660.0340.2220.0300.0000.0000.0660.0880.1610.0000.1380.1100.0000.0210.4751.0000.0600.122
Street1.0000.0000.0970.0000.0470.0000.0000.0000.1110.0380.1890.0000.0000.1120.1670.0620.0000.0000.0000.0000.1720.0480.0570.0320.0240.0000.0340.0000.0000.1060.0000.0000.0690.0910.0000.0000.0000.3400.3081.0000.2370.1820.0000.0000.2250.0601.0000.000
YrSold0.1340.0000.0000.0000.0160.0000.0000.0110.0100.0000.0330.0000.0350.0200.0000.0000.0000.0000.0000.0250.0000.0360.0000.0000.0150.0370.0040.0280.0000.0000.0360.0070.0380.0000.0380.0000.0000.0630.0190.0000.0760.0000.0000.0000.0970.1220.0001.000

Missing values

2025-06-03T13:16:49.829561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

Sample

IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleCondition
0146120RH80.011622PaveNaNRegLvlAllPubInsideGtlNAmesFeedrNorm1Fam1Story5619611961GableCompShgVinylSdVinylSdNaN0.0TATACBlockTATANoRec468.0LwQ144.0270.0882.0GasATAYSBrkr896008960.00.01021TA5Typ0NaNAttchd1961.0Unf1.0730.0TATAY1400001200NaNMnPrvNaN062010WDNormal
1146220RL81.014267PaveNaNIR1LvlAllPubCornerGtlNAmesNormNorm1Fam1Story6619581958HipCompShgWd SdngWd SdngBrkFace108.0TATACBlockTATANoALQ923.0Unf0.0406.01329.0GasATAYSBrkr13290013290.00.01131Gd6Typ0NaNAttchd1958.0Unf1.0312.0TATAY393360000NaNNaNGar21250062010WDNormal
2146360RL74.013830PaveNaNIR1LvlAllPubInsideGtlGilbertNormNorm1Fam2Story5519971998GableCompShgVinylSdVinylSdNaN0.0TATAPConcGdTANoGLQ791.0Unf0.0137.0928.0GasAGdYSBrkr928701016290.00.02131TA6Typ1TAAttchd1997.0Fin2.0482.0TATAY212340000NaNMnPrvNaN032010WDNormal
3146460RL78.09978PaveNaNIR1LvlAllPubInsideGtlGilbertNormNorm1Fam2Story6619981998GableCompShgVinylSdVinylSdBrkFace20.0TATAPConcTATANoGLQ602.0Unf0.0324.0926.0GasAExYSBrkr926678016040.00.02131Gd7Typ1GdAttchd1998.0Fin2.0470.0TATAY360360000NaNNaNNaN062010WDNormal
41465120RL43.05005PaveNaNIR1HLSAllPubInsideGtlStoneBrNormNormTwnhsE1Story8519921992GableCompShgHdBoardHdBoardNaN0.0GdTAPConcGdTANoALQ263.0Unf0.01017.01280.0GasAExYSBrkr12800012800.00.02021Gd5Typ0NaNAttchd1992.0RFn2.0506.0TATAY082001440NaNNaNNaN012010WDNormal
5146660RL75.010000PaveNaNIR1LvlAllPubCornerGtlGilbertNormNorm1Fam2Story6519931994GableCompShgHdBoardHdBoardNaN0.0TATAPConcGdTANoUnf0.0Unf0.0763.0763.0GasAGdYSBrkr763892016550.00.02131TA7Typ1TAAttchd1993.0Fin2.0440.0TATAY157840000NaNNaNNaN042010WDNormal
6146720RLNaN7980PaveNaNIR1LvlAllPubInsideGtlGilbertNormNorm1Fam1Story6719922007GableCompShgHdBoardHdBoardNaN0.0TAGdPConcGdTANoALQ935.0Unf0.0233.01168.0GasAExYSBrkr11870011871.00.02031TA6Typ0NaNAttchd1992.0Fin2.0420.0TATAY483210000NaNGdPrvShed50032010WDNormal
7146860RL63.08402PaveNaNIR1LvlAllPubInsideGtlGilbertNormNorm1Fam2Story6519981998GableCompShgVinylSdVinylSdNaN0.0TATAPConcGdTANoUnf0.0Unf0.0789.0789.0GasAGdYSBrkr789676014650.00.02131TA7Typ1GdAttchd1998.0Fin2.0393.0TATAY0750000NaNNaNNaN052010WDNormal
8146920RL85.010176PaveNaNRegLvlAllPubInsideGtlGilbertNormNorm1Fam1Story7519901990GableCompShgHdBoardHdBoardNaN0.0TATAPConcGdTAGdGLQ637.0Unf0.0663.01300.0GasAGdYSBrkr13410013411.00.01121Gd5Typ1PoAttchd1990.0Unf2.0506.0TATAY19200000NaNNaNNaN022010WDNormal
9147020RL70.08400PaveNaNRegLvlAllPubCornerGtlNAmesNormNorm1Fam1Story4519701970GableCompShgPlywoodPlywoodNaN0.0TATACBlockTATANoALQ804.0Rec78.00.0882.0GasATAYSBrkr882008821.00.01021TA4Typ0NaNAttchd1970.0Fin2.0525.0TATAY24000000NaNMnPrvNaN042010WDNormal
IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleCondition
14492910180RM21.01470PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhsSFoyer4619701970GableCompShgCemntBdCmentBdNaN0.0TATACBlockGdTAAvGLQ522.0Unf0.0108.0630.0GasATAYSBrkr630006301.00.01011TA3Typ0NaNNaNNaNNaN0.00.0NaNNaNY000000NaNNaNNaN042006WDNormal
14502911160RM21.01484PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhsE2Story4419721972GableCompShgCemntBdCmentBdNaN0.0TATACBlockTATANoRec252.0Unf0.0294.0546.0GasATAYSBrkr546546010920.00.01131TA5Typ0NaNAttchd1972.0Unf1.0253.0TAFaY000000NaNNaNNaN052006WDNormal
1451291220RL80.013384PaveNaNRegLvlAllPubInsideModMitchelNormNorm1Fam1Story5519691979GableCompShgPlywoodPlywoodBrkFace194.0TATAPConcTATAAvRec119.0BLQ344.0641.01104.0GasAFaYSBrkr13600013601.00.01031TA8Typ1TAAttchd1969.0RFn1.0336.0TATAY16000000NaNNaNNaN052006WDNormal
14522913160RM21.01533PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhs2Story4519701970GableCompShgCemntBdCmentBdNaN0.0TATACBlockTATANoRec408.0Unf0.0138.0546.0GasATAYSBrkr546546010920.00.01131TA5Typ0NaNCarPort1970.0Unf1.0286.0TATAY000000NaNNaNNaN0122006WDAbnorml
14532914160RM21.01526PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhs2Story4519701970GableCompShgCemntBdCmentBdNaN0.0TATACBlockTATANoUnf0.0Unf0.0546.0546.0GasATAYSBrkr546546010920.00.01131TA5Typ0NaNNaNNaNNaN0.00.0NaNNaNY0340000NaNGdPrvNaN062006WDNormal
14542915160RM21.01936PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhs2Story4719701970GableCompShgCemntBdCmentBdNaN0.0TATACBlockTATANoUnf0.0Unf0.0546.0546.0GasAGdYSBrkr546546010920.00.01131TA5Typ0NaNNaNNaNNaN0.00.0NaNNaNY000000NaNNaNNaN062006WDNormal
14552916160RM21.01894PaveNaNRegLvlAllPubInsideGtlMeadowVNormNormTwnhsE2Story4519701970GableCompShgCemntBdCmentBdNaN0.0TATACBlockTATANoRec252.0Unf0.0294.0546.0GasATAYSBrkr546546010920.00.01131TA6Typ0NaNCarPort1970.0Unf1.0286.0TATAY0240000NaNNaNNaN042006WDAbnorml
1456291720RL160.020000PaveNaNRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story5719601996GableCompShgVinylSdVinylSdNaN0.0TATACBlockTATANoALQ1224.0Unf0.00.01224.0GasAExYSBrkr12240012241.00.01041TA7Typ1TADetchd1960.0Unf2.0576.0TATAY47400000NaNNaNNaN092006WDAbnorml
1457291885RL62.010441PaveNaNRegLvlAllPubInsideGtlMitchelNormNorm1FamSFoyer5519921992GableCompShgHdBoardWd ShngNaN0.0TATAPConcGdTAAvGLQ337.0Unf0.0575.0912.0GasATAYSBrkr970009700.01.01031TA6Typ0NaNNaNNaNNaN0.00.0NaNNaNY80320000NaNMnPrvShed70072006WDNormal
1458291960RL74.09627PaveNaNRegLvlAllPubInsideModMitchelNormNorm1Fam2Story7519931994GableCompShgHdBoardHdBoardBrkFace94.0TATAPConcGdTAAvLwQ758.0Unf0.0238.0996.0GasAExYSBrkr9961004020000.00.02131TA9Typ1TAAttchd1993.0Fin3.0650.0TATAY190480000NaNNaNNaN0112006WDNormal